
import pandas as pd 
# 预处理
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification, make_moons
from sklearn.model_selection import cross_validate, StratifiedKFold
from sklearn.metrics import average_precision_score

import numpy as np
import pandas as pd
from collections import Counter

from sklearn.ensemble import RandomForestClassifier

from imblearn.datasets import make_imbalance
from imblearn.over_sampling import SMOTE, ADASYN

from sklearn.decomposition import PCA, KernelPCA
from sklearn.manifold import Isomap, MDS, TSNE
from sklearn.preprocessing import StandardScaler
import seaborn as sns
# from visualize import viz_data
# 数据增强
import numpy as np

# 建模比较
from sklearn.utils import shuffle        #用于打乱表格，使用于split函数之前
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.model_selection import learning_curve
from sklearn.model_selection import validation_curve
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.naive_bayes import BernoulliNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.feature_selection import RFE,RFECV
from sklearn import tree
from sklearn.naive_bayes import BernoulliNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score

from sklearn import metrics
from sklearn.metrics import roc_curve, auc,roc_auc_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

# 采样算法
# from imblearn.datasets import make_imbalance          
from imblearn.over_sampling import KMeansSMOTE,SMOTE,SVMSMOTE                        # 增强采样
from imblearn.under_sampling import AllKNN,CondensedNearestNeighbour,NearMiss        # 欠 采样
from imblearn.combine import SMOTEENN,SMOTETomek

# 忽略烦人的红色提示
import warnings
warnings.filterwarnings("ignore")
pd.set_option('max_columns',1000)
pd.set_option('max_row',1000)

## 图像显示中文的问题
import matplotlib
matplotlib.rcParams['axes.unicode_minus']=False
import matplotlib.pyplot as plt


# 第二波建模
from sklearn.linear_model import RidgeClassifier,LogisticRegressionCV, Perceptron
from sklearn.linear_model import SGDClassifier


df = pd.read_csv("data_original_waited_for_model.csv")
df


# 得分计算函数
def round_score(score):
    return np.round(score, 3)


# 降维展示函数
def data_enhance_display(Xtest, df_D, fig_names,location):
    """
    Xtest：去重的多维度特征数据
    df_D：与Xtest对应的 标签数据，两列，一个是Label 一个是最后一列表明是不是增强采样得到的
    fig_name: 绘制图像的名字
    """

    # 实例化降维器
    pca = PCA(n_components=2, random_state=123)
    
    kpca = KernelPCA(
        n_components=2,
        kernel="poly",  ## 核函数为rbf核
        gamma=0.2,
        random_state=123)
    
    isomap = Isomap(
        n_neighbors=2,  ## 每个点考虑的近邻数量
        n_components=2)  ## 降维到3维空间中
    
    tsne = TSNE(
        n_components=2,
        perplexity=25,  ## TSNE进行数据的降维,降维到3维空间中
        early_exaggeration=3,
        random_state=123)
    
    mds = MDS(n_components=2, dissimilarity="euclidean",
              random_state=123)  ## MDS进行数据的降维,降维到3维空间中

    model_estimators = [pca, kpca, isomap, tsne, mds]

    Xtest_tmp = StandardScaler().fit_transform(Xtest)
    # 五种方法进行循环展示
    for (name, method) in zip(fig_names, model_estimators): #五种循环的方法
#         print(Xtest)
        tmp_X = method.fit_transform(Xtest_tmp)
        X_test_MDS = pd.DataFrame(tmp_X,columns=["Features_1","Features_2"])
        
        df_fig = pd.concat([df_D,X_test_MDS],axis=1)
        plt.figure(dpi= 100)
        sns.set_style("whitegrid")

        #尝试修改一下颜色
        pall = sns.color_palette(['#73a2c6', '#f4777f'])
        sns.set_palette(pall)

        sns.scatterplot(data=df_fig, x="Features_1", y="Features_2", hue="label")
        plt.xticks(fontsize=12); plt.yticks(fontsize=12)
        plt.title(name+" Display", fontsize=18)
        plt.xlabel("Feature_1",fontsize=15)
        plt.ylabel("Feature_2",fontsize=15)
        plt.savefig(location+name+".png",bbox_inches="tight")
        plt.show()


fig_names = ["PCA", "KPCA", "ISOMAP", "T-SNE", "MDS"]
# fig_names = ["ISOMAP","T-SNE","MDS"]

## 模型评价函数===========================================
def cv_score(X_train, y_train, random_state=42):
    model = RandomForestClassifier(n_estimators=1000, max_depth=9, random_state=random_state)
    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)

    score_funcs = [
        'accuracy',
        'precision',
        'recall',
        'roc_auc'
    ]

    scores = cross_validate(model, X_train, y_train, scoring=score_funcs, cv=cv, return_estimator=True)
    print('Mean Accuracy:', round_score(scores['test_accuracy'].mean()))
    print('Mean Precision:', round_score(scores['test_precision'].mean()))
    print('Mean Recall:', round_score(scores['test_recall'].mean()))
    print('Mean ROC AUC:', round_score(scores['test_roc_auc'].mean()))
    
    return scores

def predict_mean_score(scores, X_test):
    pred_scores = []
    for i in range(len(scores['estimator'])):
        model = scores['estimator'][i]
        pred = model.predict_proba(X_test)
        pred_scores.append(pred)
    pred_score = np.mean(pred_scores, axis=0)
    
    return pred_score


from scipy import stats
# p-value比较函数
def pvalue(df,X):
    """
    df 总 DataFrame
    X 是X
    """
    feature_list = []
    feature_values = []
    for i in X.columns.values:
        # 合并做总表
        df_X = df[i]
        plotdata = pd.concat([Y,df_X],axis=1)
        # 提取出两个部分的数据
        plotdata_umami = plotdata.query('label == 1')
        plotdata_bitter = plotdata.query('label == 0')
    #     print(plotdata_bitter)
        # 通过K-S检验判断是不是同分布
        static, value= stats.ks_2samp(plotdata_umami[i],plotdata_bitter[i])
        print(f"{i},,,,,{stats.ks_2samp(plotdata_umami[i],plotdata_bitter[i])}")
        feature_values.append(value)
        if value <= 0.05:
            feature_list.append(i)
    print(feature_list);print(len(feature_list))

    #绘制一下values的分布情况
    fig = sns.kdeplot(feature_values, shade=True, color="g", label="Cyl=4", alpha=.7)
    return feature_list,fig

# 试试自己的颜色
import matplotlib
import matplotlib.colors as col
import matplotlib.cm as cm
import matplotlib.pyplot as plt
def heatmap_fig(df,location):
    # 热图比较函数
    cmap2 = col.LinearSegmentedColormap.from_list('red_blue_j', [
        '#5681b9', '#73a2c6', '#93c4d2', '#b9e5dd', '#ffd3bf', '#ffa59e',
        '#f4777f', '#dd4c65'
    ])
    # extra arguments are N=256, gamma=1.0
    cm.register_cmap(cmap=cmap2)
    # we can skip name here as it was already defined
    cm.get_cmap('red_blue_j')
    plt.figure(figsize=(6, 5),dpi=300)
    fig = sns.heatmap(df.corr(), cmap="red_blue_j")
    plt.savefig(location,bbox_inches="tight") 
    return fig


df = pd.read_csv("data_original_waited_for_model.csv")
df
x = df.iloc[:,:-1]   # 不要最后一列的数据
Y = df[["label"]]     # 提取标签
columns = x.columns
columns

Index(['f1-F', 'f2-F', 'f3-F', 'f4-F', 'f5-F', 'f6-F', 'f7-F', 'f8-F', 'f9-F',
       'f10-F',
       ...
       'f16-PO', 'f17-PO', 'f18-PO', 'f19-PO', 'f20-PO', 'f21-PO', 'f22-PO',
       'f23-PO', 'f24-PO', 'f25-PO'],
      dtype='object', length=125)


# 得到label的数量
counter = Counter(df["label"])
counter

Counter({0: 39, 1: 231})

pwd

'C:\\Users\\Alex\\Desktop\\00-分子模拟自己写的教参\\77_计算机笔记\\科研代码保管\\doc.-ben_-wu\\Code_Final_原始数据'


pal2 = sns.color_palette(['#FE817D', '#81B8DF'])
sns.set_palette(pal2)

fig = plt.figure(figsize=(30,20))
for ii, name in enumerate(columns):
    plt.subplot(10, 13, ii + 1)
    sns.kdeplot(data=df, x=name, hue="label", lw=3, bw=0.4)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\0_原始数据\\全分布图.png",bbox_inches="tight")
plt.show()


pal2 = sns.color_palette(['#FE817D', '#81B8DF'])
sns.set_palette(pal2)

fig = plt.figure(figsize=(30,20))
for ii, name in enumerate(columns):
    plt.subplot(10, 13, ii + 1)
    sns.kdeplot(data=df, x=name, hue="label", lw=3, bw=0.4, log_scale=True)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\0_原始数据\\log化全分布图.png",bbox_inches="tight")
plt.show()


location = "Fig\\0_原始数据\\"
data_enhance_display(x, Y, fig_names,location)


df_original_tmp,original_fig = pvalue(df,x)
df_original_tmp

f1-F,,,,,KstestResult(statistic=0.18048618048618048, pvalue=0.1989808059635899)
f2-F,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f3-F,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f4-F,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f5-F,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f6-F,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f7-F,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f8-F,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f9-F,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f10-F,,,,,KstestResult(statistic=0.16716616716616717, pvalue=0.2737527794107313)
f11-F,,,,,KstestResult(statistic=0.17582417582417584, pvalue=0.2231826795002836)
f12-F,,,,,KstestResult(statistic=0.19314019314019315, pvalue=0.14359992233012842)
f13-F,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f14-F,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f15-F,,,,,KstestResult(statistic=0.17116217116217117, pvalue=0.2494750432844094)
f16-F,,,,,KstestResult(statistic=0.13186813186813187, pvalue=0.5570760454498898)
f17-F,,,,,KstestResult(statistic=0.11222111222111222, pvalue=0.7489911088767807)
f18-F,,,,,KstestResult(statistic=0.12420912420912421, pvalue=0.6313710650692834)
f19-F,,,,,KstestResult(statistic=0.12753912753912755, pvalue=0.5986679552758554)
f20-F,,,,,KstestResult(statistic=0.12520812520812521, pvalue=0.6230095492637635)
f21-F,,,,,KstestResult(statistic=0.11888111888111888, pvalue=0.683214564120426)
f22-F,,,,,KstestResult(statistic=0.11222111222111222, pvalue=0.7489911088767807)
f23-F,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f24-F,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f25-F,,,,,KstestResult(statistic=0.15484515484515485, pvalue=0.3592502445630069)
f1-LT,,,,,KstestResult(statistic=0.17216117216117216, pvalue=0.2436041598629427)
f2-LT,,,,,KstestResult(statistic=0.18048618048618048, pvalue=0.1989808059635899)
f3-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f4-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f5-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f6-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f7-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f8-LT,,,,,KstestResult(statistic=0.17182817182817184, pvalue=0.245555117204884)
f9-LT,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f10-LT,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f11-LT,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f12-LT,,,,,KstestResult(statistic=0.14119214119214119, pvalue=0.4714063955847605)
f13-LT,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f14-LT,,,,,KstestResult(statistic=0.13253413253413254, pvalue=0.5508117921225864)
f15-LT,,,,,KstestResult(statistic=0.14152514152514153, pvalue=0.4686992923045229)
f16-LT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f17-LT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f18-LT,,,,,KstestResult(statistic=0.1575091575091575, pvalue=0.3391923405201712)
f19-LT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f20-LT,,,,,KstestResult(statistic=0.13386613386613386, pvalue=0.539661807036091)
f21-LT,,,,,KstestResult(statistic=0.13386613386613386, pvalue=0.539661807036091)
f22-LT,,,,,KstestResult(statistic=0.12953712953712954, pvalue=0.5809611285100584)
f23-LT,,,,,KstestResult(statistic=0.14485514485514486, pvalue=0.43936959339117543)
f24-LT,,,,,KstestResult(statistic=0.13186813186813187, pvalue=0.5570760454498898)
f25-LT,,,,,KstestResult(statistic=0.12520812520812521, pvalue=0.6230095492637635)
f1-RT,,,,,KstestResult(statistic=0.16317016317016317, pvalue=0.2997146191636899)
f2-RT,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f3-RT,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f4-RT,,,,,KstestResult(statistic=0.14119214119214119, pvalue=0.4714063955847605)
f5-RT,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f6-RT,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f7-RT,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f8-RT,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f9-RT,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f10-RT,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f11-RT,,,,,KstestResult(statistic=0.14585414585414586, pvalue=0.431263797602371)
f12-RT,,,,,KstestResult(statistic=0.1495171495171495, pvalue=0.40063482241563464)
f13-RT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f14-RT,,,,,KstestResult(statistic=0.12853812853812854, pvalue=0.5893568256478232)
f15-RT,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f16-RT,,,,,KstestResult(statistic=0.1405261405261405, pvalue=0.4771529757802586)
f17-RT,,,,,KstestResult(statistic=0.13153513153513152, pvalue=0.5606039099897078)
f18-RT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f19-RT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f20-RT,,,,,KstestResult(statistic=0.15517815517815517, pvalue=0.3568431427482085)
f21-RT,,,,,KstestResult(statistic=0.15084915084915085, pvalue=0.3905073695876212)
f22-RT,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f23-RT,,,,,KstestResult(statistic=0.14185814185814186, pvalue=0.46599879895337226)
f24-RT,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f25-RT,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f1-C,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f2-C,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f3-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f4-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f5-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f6-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f7-C,,,,,KstestResult(statistic=0.17582417582417584, pvalue=0.2231826795002836)
f8-C,,,,,KstestResult(statistic=0.1714951714951715, pvalue=0.24751808080866655)
f9-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f10-C,,,,,KstestResult(statistic=0.1714951714951715, pvalue=0.24751808080866655)
f11-C,,,,,KstestResult(statistic=0.16716616716616717, pvalue=0.2737527794107313)
f12-C,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f13-C,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f14-C,,,,,KstestResult(statistic=0.13286713286713286, pvalue=0.5480084572089498)
f15-C,,,,,KstestResult(statistic=0.12853812853812854, pvalue=0.5893568256478232)
f16-C,,,,,KstestResult(statistic=0.12887112887112886, pvalue=0.5865455522276334)
f17-C,,,,,KstestResult(statistic=0.12387612387612387, pvalue=0.634211445553662)
f18-C,,,,,KstestResult(statistic=0.10722610722610723, pvalue=0.7938108065448906)
f19-C,,,,,KstestResult(statistic=0.1495171495171495, pvalue=0.40063482241563464)
f20-C,,,,,KstestResult(statistic=0.1341991341991342, pvalue=0.5369268624365809)
f21-C,,,,,KstestResult(statistic=0.12121212121212122, pvalue=0.6626575921953571)
f22-C,,,,,KstestResult(statistic=0.15584415584415584, pvalue=0.35215878844464754)
f23-C,,,,,KstestResult(statistic=0.16017316017316016, pvalue=0.3206633295366209)
f24-C,,,,,KstestResult(statistic=0.1561771561771562, pvalue=0.34971437882901024)
f25-C,,,,,KstestResult(statistic=0.15151515151515152, pvalue=0.38560272095138715)
f1-PO,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f2-PO,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f3-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f4-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f5-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f6-PO,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f7-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f8-PO,,,,,KstestResult(statistic=0.14518814518814518, pvalue=0.4365461295314471)
f9-PO,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f10-PO,,,,,KstestResult(statistic=0.15817515817515818, pvalue=0.33436875480183215)
f11-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f12-PO,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f13-PO,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f14-PO,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f15-PO,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f16-PO,,,,,KstestResult(statistic=0.14152514152514153, pvalue=0.4686992923045229)
f17-PO,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f18-PO,,,,,KstestResult(statistic=0.13320013320013321, pvalue=0.545210888694405)
f19-PO,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f20-PO,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f21-PO,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f22-PO,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f23-PO,,,,,KstestResult(statistic=0.11921411921411922, pvalue=0.6796833284484975)
f24-PO,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f25-PO,,,,,KstestResult(statistic=0.14185814185814186, pvalue=0.46599879895337226)
[]
0

[]


location = "Fig\\0_原始数据\\热图.png"
heatmap_2 = heatmap_fig(x,location)


X_train, X_test, y_train, y_test = train_test_split(
    x, Y, test_size=0.2, random_state=0, stratify=df["label"], shuffle=True   # y_imbalance 是 label的那一列
)
scores = cv_score(X_train, y_train, random_state=42)
pred_score = predict_mean_score(scores, X_test)
average_precision = average_precision_score(y_test, pred_score[:, 1])
print('Average precision-recall score: {0:0.2f}'.format(average_precision))

Mean Accuracy: 0.857
Mean Precision: 0.857
Mean Recall: 1.0
Mean ROC AUC: 0.465
Average precision-recall score: 0.90


# 我来做一个记录模块，但是记录结果
df_original = pd.DataFrame({
    "Accuracy": [round_score(scores['test_accuracy'].mean())],
    "Precision": [round_score(scores['test_precision'].mean())],
    "Recall": [round_score(scores['test_recall'].mean())],
    "ROC AUC": [round_score(scores['test_roc_auc'].mean())]
})
df_original


tmp_original = df_original.T
tmp_original.columns = ['original']
tmp_original


fig_names = ["PCA","KPCA"]
pca = PCA(n_components = 15,random_state = 123)
kpca = KernelPCA(n_components = 15,kernel = "poly", ## 核函数为rbf核
             gamma = 0.2,random_state = 123)
model_estimators = [pca,kpca]
for (name, method) in zip(fig_names, model_estimators): #五种循环的方法
    method.fit(x)

    if name == "PCA":
        exvar = method.explained_variance_
    if name =="KPCA":
        exvar = method.lambdas_

    plt.figure(dpi= 80)       #figsize=()
    plt.plot(exvar,"r-o") 
    plt.hlines(y = 1, xmin = 0, xmax = 8)
    plt.xlabel("Feature_NUM",fontsize=15)
    plt.ylabel("Var",fontsize=15)
    plt.title(name, fontsize=18)
    plt.savefig("Fig\\1_原始数据_pca\\"+name+"损失函数.png",bbox_inches="tight")
    plt.show()


score_funcs = [
        'accuracy',
        'precision',
        'recall',
        'roc_auc',
        "precision-recall"]
tmp = np.zeros((10,5))
df_scores = pd.DataFrame(tmp,index=range(1,11),columns=score_funcs)   #  这样得到一个十轮的函数

for i in range(1, 11):
    df = pd.read_csv("data_original_waited_for_model.csv")
    x = df.iloc[:, :-1]  # 不要最后一列的数据
    Y = df[["label"]]  # 提取标签
    print(f"这是第{i}维度PCA")
    pca = PCA(n_components=i, random_state=123)
    x_tmp = pca.fit_transform(x)
    X_train, X_test, y_train, y_test = train_test_split(
        x_tmp,
        Y,
        test_size=0.2,
        random_state=0,
        stratify=Y,
        shuffle=True  # y_imbalance 是 label的那一列
    )
    scores = cv_score(X_train, y_train, random_state=42)
    
    # 开始给df 
    for name in score_funcs:
        if name != "precision-recall":
            df_scores[name].iloc[i-1] = round_score(scores["test_"+name].mean())
        else:
            pred_score = predict_mean_score(scores, X_test)
            average_precision = average_precision_score(y_test, pred_score[:, 1])
            print('Average precision-recall score: {0:0.2f}'.format(average_precision))
            df_scores["precision-recall"].iloc[i-1]=round_score(average_precision)

df_scores

这是第1维度PCA
Mean Accuracy: 0.746
Mean Precision: 0.837
Mean Recall: 0.87
Mean ROC AUC: 0.428
Average precision-recall score: 0.95
这是第2维度PCA
Mean Accuracy: 0.838
Mean Precision: 0.857
Mean Recall: 0.973
Mean ROC AUC: 0.432
Average precision-recall score: 0.90
这是第3维度PCA
Mean Accuracy: 0.847
Mean Precision: 0.855
Mean Recall: 0.989
Mean ROC AUC: 0.493
Average precision-recall score: 0.87
这是第4维度PCA
Mean Accuracy: 0.852
Mean Precision: 0.856
Mean Recall: 0.995
Mean ROC AUC: 0.45
Average precision-recall score: 0.86
这是第5维度PCA
Mean Accuracy: 0.847
Mean Precision: 0.855
Mean Recall: 0.989
Mean ROC AUC: 0.55
Average precision-recall score: 0.90
这是第6维度PCA
Mean Accuracy: 0.842
Mean Precision: 0.858
Mean Recall: 0.978
Mean ROC AUC: 0.54
Average precision-recall score: 0.93
这是第7维度PCA
Mean Accuracy: 0.838
Mean Precision: 0.854
Mean Recall: 0.978
Mean ROC AUC: 0.513
Average precision-recall score: 0.92
这是第8维度PCA
Mean Accuracy: 0.838
Mean Precision: 0.854
Mean Recall: 0.978
Mean ROC AUC: 0.512
Average precision-recall score: 0.94
这是第9维度PCA
Mean Accuracy: 0.833
Mean Precision: 0.853
Mean Recall: 0.973
Mean ROC AUC: 0.51
Average precision-recall score: 0.94
这是第10维度PCA
Mean Accuracy: 0.838
Mean Precision: 0.854
Mean Recall: 0.978
Mean ROC AUC: 0.489
Average precision-recall score: 0.94


df_scores["label"]=1
for i in range(1,11):
    df_scores["label"].iloc[i-1] = i
df_scores


df_tmp = df_scores.melt(id_vars="label")
df_tmp


import matplotlib.pyplot as plt

fig = plt.figure(figsize=(6, 5),dpi=300)
sns.pointplot(x="label", y="value", hue="variable",
#             palette={"original": "#5681b9", "smote": "#dd4c65"},
#             markers=["^", "o"], linestyles=["-", "--"],
            data=df_tmp)

# ax.spines['top'].set_visible(False)
# ax.spines['right'].set_visible(False)
plt.savefig("Fig\\1_原始数据_pca\\n=6选择依据.png",bbox_inches="tight")
# 答案基本上选6个


df = pd.read_csv("data_original_waited_for_model.csv")
x = df.iloc[:, :-1]  # 不要最后一列的数据
Y = df[["label"]]  # 提取标签
print(f"这是第{6}维度PCA")
pca = PCA(n_components=6, random_state=123)
x_tmp = pca.fit_transform(x)
x_tmp = pd.DataFrame(x_tmp,columns=range(1,7))
df_tmp = pd.concat([x_tmp,Y],axis=1)
fig = plt.figure(figsize=(10,8))
names = x_tmp.columns
for ii, name in enumerate(names):
    plt.subplot(2, 3, ii + 1)
    sns.kdeplot(data=df_tmp, x=name, hue="label", lw=3, bw=0.4)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\1_原始数据_pca\\全分布.png",bbox_inches="tight")
plt.show()

这是第6维度PCA


df = pd.read_csv("data_original_waited_for_model.csv")
x = df.iloc[:, :-1]  # 不要最后一列的数据
Y = df[["label"]]  # 提取标签
print(f"这是第{6}维度PCA")
pca = PCA(n_components=6, random_state=123)
x_tmp = pca.fit_transform(x)
x_tmp = pd.DataFrame(x_tmp,columns=range(1,7))
df_tmp = pd.concat([x_tmp,Y],axis=1)
fig = plt.figure(figsize=(10,8))
names = x_tmp.columns
for ii, name in enumerate(names):
    plt.subplot(2, 3, ii + 1)
    sns.kdeplot(data=df_tmp, x=name, hue="label", lw=3, bw=0.4, log_scale=True)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\1_原始数据_pca\\log化全分布.png",bbox_inches="tight")
plt.show()

这是第6维度PCA


fig_names = ["PCA后PCA展示", "KPCA后PCA展示", "ISOMAP后PCA展示", "T-SNE后PCA展示", "MDS后PCA展示"]
location = "Fig\\1_原始数据_pca\\"
data_enhance_display(x_tmp,Y, fig_names,location)


x_tmp = pd.DataFrame(x_tmp,columns=range(1,7))
df_original_pca,original_fig_pca = pvalue(df_tmp,x_tmp)

1,,,,,KstestResult(statistic=0.14119214119214119, pvalue=0.4714063955847605)
2,,,,,KstestResult(statistic=0.13086913086913088, pvalue=0.5679718771301696)
3,,,,,KstestResult(statistic=0.12121212121212122, pvalue=0.6626575921953571)
4,,,,,KstestResult(statistic=0.0919080919080919, pvalue=0.9133373259463751)
5,,,,,KstestResult(statistic=0.2937062937062937, pvalue=0.004775982019631364)
6,,,,,KstestResult(statistic=0.18514818514818515, pvalue=0.1768211070385266)
[5]
1


location = "Fig\\1_原始数据_pca\\热图.png"
heatmap_3 = heatmap_fig(x_tmp,location)


X_train, X_test, y_train, y_test = train_test_split(
    x_tmp, Y, test_size=0.2, random_state=0, stratify=df["label"], shuffle=True   # y_imbalance 是 label的那一列
)
scores = cv_score(X_train, y_train, random_state=42)
pred_score = predict_mean_score(scores, X_test)
average_precision = average_precision_score(y_test, pred_score[:, 1])
print('Average precision-recall score: {0:0.2f}'.format(average_precision))

Mean Accuracy: 0.842
Mean Precision: 0.858
Mean Recall: 0.978
Mean ROC AUC: 0.54
Average precision-recall score: 0.93


# 我来做一个记录模块，但是记录结果
df_original_pca = pd.DataFrame({
    "Accuracy": [round_score(scores['test_accuracy'].mean())],
    "Precision": [round_score(scores['test_precision'].mean())],
    "Recall": [round_score(scores['test_recall'].mean())],
    "ROC AUC": [round_score(scores['test_roc_auc'].mean())]
})
df_original_pca


tmp_pca = df_original_pca.T
tmp_pca.columns = ['original_pca']
tmp_pca


smote = SMOTE(random_state=123, k_neighbors=3)
x_smote, y_smote = smote.fit_resample(x,Y)
df_smote = pd.concat([x_smote,y_smote],axis=1)
df_smote.to_csv("2_data_deal_smote.csv",index=False)
print("SMOTE : ",np.unique(y_smote,return_counts = True))

SMOTE :  (array([0, 1], dtype=int64), array([231, 231], dtype=int64))


df_smote = pd.read_csv("2_data_deal_smote.csv")
fig = plt.figure(figsize=(30,20))

names = x_smote.columns
for ii, name in enumerate(names):
    plt.subplot(10, 13, ii + 1)
    sns.kdeplot(data=df_smote, x=name, hue="label", lw=3, bw=0.4)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\2_增强数据\\全分布.png",bbox_inches="tight")
plt.show()


df_smote = pd.read_csv("2_data_deal_smote.csv")
fig = plt.figure(figsize=(30,20))

names = x_smote.columns
for ii, name in enumerate(names):
    plt.subplot(10, 13, ii + 1)
    sns.kdeplot(data=df_smote, x=name, hue="label", lw=3, bw=0.4, log_scale=True)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\2_增强数据\\增强后log化全分布图.png",bbox_inches="tight")
plt.show()


fig_names = ["增强数据后PCA展示", "增强数据后KPCA", "增强数据后ISOMAP", "增强数据后T-SNE", "增强数据后MDS"]
location = "Fig\\2_增强数据\\"
data_enhance_display(x_smote, y_smote, fig_names,location)


df_steer,steer_fig = pvalue(df_smote,x_smote)

f1-F,,,,,KstestResult(statistic=0.18048618048618048, pvalue=0.1989808059635899)
f2-F,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f3-F,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f4-F,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f5-F,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f6-F,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f7-F,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f8-F,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f9-F,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f10-F,,,,,KstestResult(statistic=0.16716616716616717, pvalue=0.2737527794107313)
f11-F,,,,,KstestResult(statistic=0.17582417582417584, pvalue=0.2231826795002836)
f12-F,,,,,KstestResult(statistic=0.19314019314019315, pvalue=0.14359992233012842)
f13-F,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f14-F,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f15-F,,,,,KstestResult(statistic=0.17116217116217117, pvalue=0.2494750432844094)
f16-F,,,,,KstestResult(statistic=0.13186813186813187, pvalue=0.5570760454498898)
f17-F,,,,,KstestResult(statistic=0.11222111222111222, pvalue=0.7489911088767807)
f18-F,,,,,KstestResult(statistic=0.12420912420912421, pvalue=0.6313710650692834)
f19-F,,,,,KstestResult(statistic=0.12753912753912755, pvalue=0.5986679552758554)
f20-F,,,,,KstestResult(statistic=0.12520812520812521, pvalue=0.6230095492637635)
f21-F,,,,,KstestResult(statistic=0.11888111888111888, pvalue=0.683214564120426)
f22-F,,,,,KstestResult(statistic=0.11222111222111222, pvalue=0.7489911088767807)
f23-F,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f24-F,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f25-F,,,,,KstestResult(statistic=0.15484515484515485, pvalue=0.3592502445630069)
f1-LT,,,,,KstestResult(statistic=0.17216117216117216, pvalue=0.2436041598629427)
f2-LT,,,,,KstestResult(statistic=0.18048618048618048, pvalue=0.1989808059635899)
f3-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f4-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f5-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f6-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f7-LT,,,,,KstestResult(statistic=0.17615717615717616, pvalue=0.2213452101615908)
f8-LT,,,,,KstestResult(statistic=0.17182817182817184, pvalue=0.245555117204884)
f9-LT,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f10-LT,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f11-LT,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f12-LT,,,,,KstestResult(statistic=0.14119214119214119, pvalue=0.4714063955847605)
f13-LT,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f14-LT,,,,,KstestResult(statistic=0.13253413253413254, pvalue=0.5508117921225864)
f15-LT,,,,,KstestResult(statistic=0.14152514152514153, pvalue=0.4686992923045229)
f16-LT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f17-LT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f18-LT,,,,,KstestResult(statistic=0.1575091575091575, pvalue=0.3391923405201712)
f19-LT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f20-LT,,,,,KstestResult(statistic=0.13386613386613386, pvalue=0.539661807036091)
f21-LT,,,,,KstestResult(statistic=0.13386613386613386, pvalue=0.539661807036091)
f22-LT,,,,,KstestResult(statistic=0.12953712953712954, pvalue=0.5809611285100584)
f23-LT,,,,,KstestResult(statistic=0.14485514485514486, pvalue=0.43936959339117543)
f24-LT,,,,,KstestResult(statistic=0.13186813186813187, pvalue=0.5570760454498898)
f25-LT,,,,,KstestResult(statistic=0.12520812520812521, pvalue=0.6230095492637635)
f1-RT,,,,,KstestResult(statistic=0.16317016317016317, pvalue=0.2997146191636899)
f2-RT,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f3-RT,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f4-RT,,,,,KstestResult(statistic=0.14119214119214119, pvalue=0.4714063955847605)
f5-RT,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f6-RT,,,,,KstestResult(statistic=0.1585081585081585, pvalue=0.3320489457518442)
f7-RT,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f8-RT,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f9-RT,,,,,KstestResult(statistic=0.16283716283716285, pvalue=0.3019241196197259)
f10-RT,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f11-RT,,,,,KstestResult(statistic=0.14585414585414586, pvalue=0.431263797602371)
f12-RT,,,,,KstestResult(statistic=0.1495171495171495, pvalue=0.40063482241563464)
f13-RT,,,,,KstestResult(statistic=0.13653013653013654, pvalue=0.5133265083752802)
f14-RT,,,,,KstestResult(statistic=0.12853812853812854, pvalue=0.5893568256478232)
f15-RT,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f16-RT,,,,,KstestResult(statistic=0.1405261405261405, pvalue=0.4771529757802586)
f17-RT,,,,,KstestResult(statistic=0.13153513153513152, pvalue=0.5606039099897078)
f18-RT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f19-RT,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f20-RT,,,,,KstestResult(statistic=0.15517815517815517, pvalue=0.3568431427482085)
f21-RT,,,,,KstestResult(statistic=0.15084915084915085, pvalue=0.3905073695876212)
f22-RT,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f23-RT,,,,,KstestResult(statistic=0.14185814185814186, pvalue=0.46599879895337226)
f24-RT,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f25-RT,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f1-C,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f2-C,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f3-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f4-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f5-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f6-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f7-C,,,,,KstestResult(statistic=0.17582417582417584, pvalue=0.2231826795002836)
f8-C,,,,,KstestResult(statistic=0.1714951714951715, pvalue=0.24751808080866655)
f9-C,,,,,KstestResult(statistic=0.18015318015318016, pvalue=0.2006932448999763)
f10-C,,,,,KstestResult(statistic=0.1714951714951715, pvalue=0.24751808080866655)
f11-C,,,,,KstestResult(statistic=0.16716616716616717, pvalue=0.2737527794107313)
f12-C,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f13-C,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f14-C,,,,,KstestResult(statistic=0.13286713286713286, pvalue=0.5480084572089498)
f15-C,,,,,KstestResult(statistic=0.12853812853812854, pvalue=0.5893568256478232)
f16-C,,,,,KstestResult(statistic=0.12887112887112886, pvalue=0.5865455522276334)
f17-C,,,,,KstestResult(statistic=0.12387612387612387, pvalue=0.634211445553662)
f18-C,,,,,KstestResult(statistic=0.10722610722610723, pvalue=0.7938108065448906)
f19-C,,,,,KstestResult(statistic=0.1495171495171495, pvalue=0.40063482241563464)
f20-C,,,,,KstestResult(statistic=0.1341991341991342, pvalue=0.5369268624365809)
f21-C,,,,,KstestResult(statistic=0.12121212121212122, pvalue=0.6626575921953571)
f22-C,,,,,KstestResult(statistic=0.15584415584415584, pvalue=0.35215878844464754)
f23-C,,,,,KstestResult(statistic=0.16017316017316016, pvalue=0.3206633295366209)
f24-C,,,,,KstestResult(statistic=0.1561771561771562, pvalue=0.34971437882901024)
f25-C,,,,,KstestResult(statistic=0.15151515151515152, pvalue=0.38560272095138715)
f1-PO,,,,,KstestResult(statistic=0.15884115884115885, pvalue=0.3297218800956546)
f2-PO,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f3-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f4-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f5-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f6-PO,,,,,KstestResult(statistic=0.14985014985014986, pvalue=0.39809213291666157)
f7-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f8-PO,,,,,KstestResult(statistic=0.14518814518814518, pvalue=0.4365461295314471)
f9-PO,,,,,KstestResult(statistic=0.15451215451215453, pvalue=0.3616800832048548)
f10-PO,,,,,KstestResult(statistic=0.15817515817515818, pvalue=0.33436875480183215)
f11-PO,,,,,KstestResult(statistic=0.15384615384615385, pvalue=0.3665510147235401)
f12-PO,,,,,KstestResult(statistic=0.15417915417915418, pvalue=0.364118192620453)
f13-PO,,,,,KstestResult(statistic=0.14552114552114553, pvalue=0.4338947004526912)
f14-PO,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f15-PO,,,,,KstestResult(statistic=0.14652014652014653, pvalue=0.4260349952854452)
f16-PO,,,,,KstestResult(statistic=0.14152514152514153, pvalue=0.4686992923045229)
f17-PO,,,,,KstestResult(statistic=0.13686313686313686, pvalue=0.5104559983653444)
f18-PO,,,,,KstestResult(statistic=0.13320013320013321, pvalue=0.545210888694405)
f19-PO,,,,,KstestResult(statistic=0.15018315018315018, pvalue=0.395551865095895)
f20-PO,,,,,KstestResult(statistic=0.13353313353313354, pvalue=0.5424261802382988)
f21-PO,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f22-PO,,,,,KstestResult(statistic=0.13786213786213786, pvalue=0.502186191542284)
f23-PO,,,,,KstestResult(statistic=0.11921411921411922, pvalue=0.6796833284484975)
f24-PO,,,,,KstestResult(statistic=0.13719613719613719, pvalue=0.5076902938639565)
f25-PO,,,,,KstestResult(statistic=0.14185814185814186, pvalue=0.46599879895337226)
[]
0


location = "Fig\\2_增强数据\\热图.png"
heatmap_4 = heatmap_fig(x_smote,location)


X_train, X_test, y_train, y_test = train_test_split(
   x_smote, y_smote, test_size=0.2, random_state=0, stratify=y_smote['label'], shuffle=True   # y_imbalance 是 label的那一列
)
scores = cv_score(X_train, y_train, random_state=42)
pred_score = predict_mean_score(scores, X_test)
average_precision = average_precision_score(y_test, pred_score[:, 1])
print('Average precision-recall score: {0:0.2f}'.format(average_precision))

Mean Accuracy: 0.819
Mean Precision: 0.841
Mean Recall: 0.784
Mean ROC AUC: 0.911
Average precision-recall score: 0.95


# 我来做一个记录模块，但是记录结果
df_steer = pd.DataFrame({
    "Accuracy": [round_score(scores['test_accuracy'].mean())],
    "Precision": [round_score(scores['test_precision'].mean())],
    "Recall": [round_score(scores['test_recall'].mean())],
    "ROC AUC": [round_score(scores['test_roc_auc'].mean())]
})
df_steer
tmp_steer = df_steer.T
tmp_steer.columns = ['steer']
tmp_steer


df = pd.read_csv("2_data_deal_smote.csv")
x = df.iloc[:, :-1]  # 不要最后一列的数据
Y = df[["label"]]  # 提取标签
print(f"这是第{10}维度PCA")

fig_names = ["PCA","KPCA"]

pca = PCA(n_components = 15,random_state = 123)
kpca = KernelPCA(n_components = 15,kernel = "poly", ## 核函数为rbf核
             gamma = 0.2,random_state = 123)
model_estimators = [pca,kpca]
for (name, method) in zip(fig_names, model_estimators): #五种循环的方法
    method.fit(x)

    if name == "PCA":
        exvar = method.explained_variance_
    if name =="KPCA":
        exvar = method.lambdas_

    plt.figure(dpi= 80)       #figsize=()
    plt.plot(exvar,"r-o") 
    plt.hlines(y = 1, xmin = 0, xmax = 8)
    plt.xlabel("Feature_NUM",fontsize=15)
    plt.ylabel("Var",fontsize=15)
    plt.title(name, fontsize=18)
    plt.savefig("Fig\\3_增强数据_pca\\"+name+"损失函数.png",bbox_inches="tight")
    plt.show()

这是第10维度PCA


score_funcs = [
        'accuracy',
        'precision',
        'recall',
        'roc_auc',
        "precision-recall"]
tmp = np.zeros((12,5))
df_scores = pd.DataFrame(tmp,index=range(1,13),columns=score_funcs)   #  这样得到一个十轮的函数

for i in range(1, 13):
    df = pd.read_csv("data_original_waited_for_model.csv")
    x = x_smote  # 不要最后一列的数据
    Y = y_smote  # 提取标签
    print(f"这是第{i}维度PCA")
    pca = PCA(n_components=i, random_state=123)
    x_tmp = pca.fit_transform(x)
    X_train, X_test, y_train, y_test = train_test_split(
        x_tmp,
        Y,
        test_size=0.2,
        random_state=0,
        stratify=Y,
        shuffle=True  # y_imbalance 是 label的那一列
    )
    scores = cv_score(X_train, y_train, random_state=42)
    
    # 开始给df 
    for name in score_funcs:
        if name != "precision-recall":
            df_scores[name].iloc[i-1] = round_score(scores["test_"+name].mean())
        else:
            pred_score = predict_mean_score(scores, X_test)
            average_precision = average_precision_score(y_test, pred_score[:, 1])
            print('Average precision-recall score: {0:0.2f}'.format(average_precision))
            df_scores["precision-recall"].iloc[i-1]=round_score(average_precision)

df_scores

这是第1维度PCA
Mean Accuracy: 0.612
Mean Precision: 0.625
Mean Recall: 0.578
Mean ROC AUC: 0.652
Average precision-recall score: 0.62
这是第2维度PCA
Mean Accuracy: 0.653
Mean Precision: 0.657
Mean Recall: 0.649
Mean ROC AUC: 0.724
Average precision-recall score: 0.88
这是第3维度PCA
Mean Accuracy: 0.71
Mean Precision: 0.717
Mean Recall: 0.708
Mean ROC AUC: 0.803
Average precision-recall score: 0.89
这是第4维度PCA
Mean Accuracy: 0.718
Mean Precision: 0.747
Mean Recall: 0.676
Mean ROC AUC: 0.801
Average precision-recall score: 0.87
这是第5维度PCA
Mean Accuracy: 0.74
Mean Precision: 0.762
Mean Recall: 0.719
Mean ROC AUC: 0.826
Average precision-recall score: 0.90
这是第6维度PCA
Mean Accuracy: 0.77
Mean Precision: 0.795
Mean Recall: 0.746
Mean ROC AUC: 0.859
Average precision-recall score: 0.90
这是第7维度PCA
Mean Accuracy: 0.764
Mean Precision: 0.785
Mean Recall: 0.746
Mean ROC AUC: 0.851
Average precision-recall score: 0.90
这是第8维度PCA
Mean Accuracy: 0.773
Mean Precision: 0.804
Mean Recall: 0.746
Mean ROC AUC: 0.864
Average precision-recall score: 0.91
这是第9维度PCA
Mean Accuracy: 0.797
Mean Precision: 0.81
Mean Recall: 0.784
Mean ROC AUC: 0.891
Average precision-recall score: 0.91
这是第10维度PCA
Mean Accuracy: 0.808
Mean Precision: 0.809
Mean Recall: 0.811
Mean ROC AUC: 0.897
Average precision-recall score: 0.93
这是第11维度PCA
Mean Accuracy: 0.81
Mean Precision: 0.812
Mean Recall: 0.811
Mean ROC AUC: 0.895
Average precision-recall score: 0.94
这是第12维度PCA
Mean Accuracy: 0.816
Mean Precision: 0.816
Mean Recall: 0.822
Mean ROC AUC: 0.897
Average precision-recall score: 0.93


df_scores["label"]=1
for i in range(1,13):
    df_scores["label"].iloc[i-1] = i

df_tmp = df_scores.melt(id_vars="label")
df_tmp


import matplotlib.pyplot as plt

fig = plt.figure(figsize=(6, 5),dpi=200)
ax1 = fig.add_subplot(1, 1, 1);ax1.set_title("Comparison of data enhancement results",fontsize=22)

sns.pointplot(x="label", y="value", hue="variable",
#             palette={"original": "#5681b9", "smote": "#dd4c65"},
#             markers=["^", "o"], linestyles=["-", "--"],
            data=df_tmp)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
plt.savefig("Fig\\3_增强数据_pca\\n=10选择依据.png",bbox_inches="tight")
# 答案基本上选10个


df = pd.read_csv("2_data_deal_smote.csv")
x = df.iloc[:, :-1]  # 不要最后一列的数据
Y = df[["label"]]  # 提取标签
print(f"这是第{10}维度PCA")
pca = PCA(n_components=10, random_state=123)
x_tmp = pca.fit_transform(x)
x_tmp = pd.DataFrame(x_tmp,columns=range(1,11))
df_tmp = pd.concat([x_tmp,Y],axis=1)
df_tmp.to_csv("data_smote_pca_10.csv",index=False)
fig = plt.figure(figsize=(20,8))
names = x_tmp.columns
for ii, name in enumerate(names):
    plt.subplot(2, 5, ii + 1)
    sns.kdeplot(data=df_tmp, x=name, hue="label", lw=3, bw=0.4)
    plt.title(name, fontsize=15)

plt.tight_layout()
plt.savefig("Fig\\3_增强数据_pca\\全分布.png",bbox_inches="tight")
plt.show()

这是第10维度PCA


fig_names = ["增强数据后pca_PCA展示", "增强数据后pca_KPCA", "增强数据后pca_ISOMAP", "增强数据后pca_T-SNE", "增强数据后pca_MDS"]
location = "Fig\\3_增强数据_pca\\"
data_enhance_display(x_smote, y_smote, fig_names,location)


df_steer_tmp_pca,original_fig = pvalue(df_tmp,x_tmp)
df_steer_tmp_pca

1,,,,,KstestResult(statistic=0.1341991341991342, pvalue=0.03110043315144815)
2,,,,,KstestResult(statistic=0.16017316017316016, pvalue=0.005269140283100362)
3,,,,,KstestResult(statistic=0.1471861471861472, pvalue=0.013320190858350327)
4,,,,,KstestResult(statistic=0.0735930735930736, pvalue=0.559998541869034)
5,,,,,KstestResult(statistic=0.29004329004329005, pvalue=5.727697508644916e-09)
6,,,,,KstestResult(statistic=0.09090909090909091, pvalue=0.29593614548719527)
7,,,,,KstestResult(statistic=0.06493506493506493, pvalue=0.7159571244787185)
8,,,,,KstestResult(statistic=0.06926406926406926, pvalue=0.6377266721378155)
9,,,,,KstestResult(statistic=0.19480519480519481, pvalue=0.0003004962370505667)
10,,,,,KstestResult(statistic=0.11688311688311688, pvalue=0.08516838224571074)
[1, 2, 3, 5, 9]
5

[1, 2, 3, 5, 9]


location = "Fig\\3_增强数据_pca\\热图.png"
heatmap_3 = heatmap_fig(x_tmp,location)


X_train, X_test, y_train, y_test = train_test_split(
   x_tmp, Y, test_size=0.2, random_state=0, stratify=Y, shuffle=True   # y_imbalance 是 label的那一列
)
scores = cv_score(X_train, y_train, random_state=42)
pred_score = predict_mean_score(scores, X_test)
average_precision = average_precision_score(y_test, pred_score[:, 1])
print('Average precision-recall score: {0:0.2f}'.format(average_precision))

Mean Accuracy: 0.808
Mean Precision: 0.809
Mean Recall: 0.811
Mean ROC AUC: 0.897
Average precision-recall score: 0.93


# 我来做一个记录模块，但是记录结果
df_steer_pca = pd.DataFrame({
    "Accuracy": [round_score(scores['test_accuracy'].mean())],
    "Precision": [round_score(scores['test_precision'].mean())],
    "Recall": [round_score(scores['test_recall'].mean())],
    "ROC AUC": [round_score(scores['test_roc_auc'].mean())]
})
df_steer_pca
tmp_steer_pca = df_steer_pca.T
tmp_steer_pca.columns = ['steer_pca']
tmp_steer_pca


df_all = pd.concat([tmp_original,tmp_pca,tmp_steer,tmp_steer_pca],axis=1)
df_all


df_all.columns = ["original","original_pca","smote","smote_pca"]
df_all


df_all["label"]=1
for i,name in enumerate(["Accuracy","Precision","Recall","AUC"]):
    df_all["label"].iloc[i] = name
df_all


df_df_ = df_all.melt(id_vars='label')
df_df_


import matplotlib.pyplot as plt

sns.set()
sns.set_theme(style="ticks")
fig = plt.figure(figsize=(6, 5), dpi=200)
ax1 = fig.add_subplot(1, 1, 1)
ax1.set_title("Comparison of data enhancement results", fontsize=22)
sns.pointplot(x="label",
              y="value",
              hue="variable",
              palette={
                  "original": "#2a557f",
                  "original_pca": "#45bc9c",
                  "smote": '#f05076',
                  "smote_pca": '#ffcd6e'
              },
              markers=["^", "o", "x", "P"],
              linestyles=["-", "--", ":", "-."],
              data=df_df_,
              ax=ax1)

ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
plt.savefig("Fig\\四种方法的选择.png",bbox_inches="tight")

预处理¶

加载包¶

读取数据¶

定义函数¶

原始数据探索¶

可视化¶

kde可视化¶

降维可视化¶

Pvalue比较¶

热图比较¶

原始数据建模建模比较¶

原始数据PCA研究¶

降维¶

降维的损失研究¶

降维的建模结果¶

可视化¶

kde可视化¶

降维可视化¶

Pvalue比较¶

热图比较¶

建模比较¶

增强数据¶

可视化¶

kde可视化¶

降维可视化¶

降维可视化¶

P-value¶

热图¶

建模比较¶

数据增强PCA¶

降维¶

降维的损失研究¶

降维可视化¶

pvalue比较¶

热图比较¶

建模¶

四个方法比较¶

	accuracy	precision	recall	roc_auc	precision-recall
1	0.746	0.837	0.870	0.428	0.948
2	0.838	0.857	0.973	0.432	0.895
3	0.847	0.855	0.989	0.493	0.874
4	0.852	0.856	0.995	0.450	0.862
5	0.847	0.855	0.989	0.550	0.899
6	0.842	0.858	0.978	0.540	0.927
7	0.838	0.854	0.978	0.513	0.918
8	0.838	0.854	0.978	0.512	0.935
9	0.833	0.853	0.973	0.510	0.943
10	0.838	0.854	0.978	0.489	0.945

	label	variable	value
0	1	accuracy	0.746
1	2	accuracy	0.838
2	3	accuracy	0.847
3	4	accuracy	0.852
4	5	accuracy	0.847
5	6	accuracy	0.842
6	7	accuracy	0.838
7	8	accuracy	0.838
8	9	accuracy	0.833
9	10	accuracy	0.838
10	1	precision	0.837
11	2	precision	0.857
12	3	precision	0.855
13	4	precision	0.856
14	5	precision	0.855
15	6	precision	0.858
16	7	precision	0.854
17	8	precision	0.854
18	9	precision	0.853
19	10	precision	0.854
20	1	recall	0.870
21	2	recall	0.973
22	3	recall	0.989
23	4	recall	0.995
24	5	recall	0.989
25	6	recall	0.978
26	7	recall	0.978
27	8	recall	0.978
28	9	recall	0.973
29	10	recall	0.978
30	1	roc_auc	0.428
31	2	roc_auc	0.432
32	3	roc_auc	0.493
33	4	roc_auc	0.450
34	5	roc_auc	0.550
35	6	roc_auc	0.540
36	7	roc_auc	0.513
37	8	roc_auc	0.512
38	9	roc_auc	0.510
39	10	roc_auc	0.489
40	1	precision-recall	0.948
41	2	precision-recall	0.895
42	3	precision-recall	0.874
43	4	precision-recall	0.862
44	5	precision-recall	0.899
45	6	precision-recall	0.927
46	7	precision-recall	0.918
47	8	precision-recall	0.935
48	9	precision-recall	0.943
49	10	precision-recall	0.945

	accuracy	precision	recall	roc_auc	precision-recall
1	0.612	0.625	0.578	0.652	0.619
2	0.653	0.657	0.649	0.724	0.883
3	0.710	0.717	0.708	0.803	0.893
4	0.718	0.747	0.676	0.801	0.874
5	0.740	0.762	0.719	0.826	0.897
6	0.770	0.795	0.746	0.859	0.900
7	0.764	0.785	0.746	0.851	0.898
8	0.773	0.804	0.746	0.864	0.913
9	0.797	0.810	0.784	0.891	0.906
10	0.808	0.809	0.811	0.897	0.926
11	0.810	0.812	0.811	0.895	0.938
12	0.816	0.816	0.822	0.897	0.935

	label	variable	value
0	Accuracy	original	0.857
1	Precision	original	0.857
2	Recall	original	1.000
3	AUC	original	0.465
4	Accuracy	original_pca	0.842
5	Precision	original_pca	0.858
6	Recall	original_pca	0.978
7	AUC	original_pca	0.540
8	Accuracy	smote	0.819
9	Precision	smote	0.841
10	Recall	smote	0.784
11	AUC	smote	0.911
12	Accuracy	smote_pca	0.808
13	Precision	smote_pca	0.809
14	Recall	smote_pca	0.811
15	AUC	smote_pca	0.897

	f1-F	f2-F	f3-F	f4-F	f5-F	f6-F	f7-F	f8-F	f9-F	f10-F	f11-F	f12-F	f13-F	f14-F	f15-F	f16-F	f17-F	f18-F	f19-F	f20-F	f21-F	f22-F	f23-F	f24-F	f25-F	f1-LT	f2-LT	f3-LT	f4-LT	f5-LT	f6-LT	f7-LT	f8-LT	f9-LT	f10-LT	f11-LT	f12-LT	f13-LT	f14-LT	f15-LT	f16-LT	f17-LT	f18-LT	f19-LT	f20-LT	f21-LT	f22-LT	f23-LT	f24-LT	f25-LT	f1-RT	f2-RT	f3-RT	f4-RT	f5-RT	f6-RT	f7-RT	f8-RT	f9-RT	f10-RT	f11-RT	f12-RT	f13-RT	f14-RT	f15-RT	f16-RT	f17-RT	f18-RT	f19-RT	f20-RT	f21-RT	f22-RT	f23-RT	f24-RT	f25-RT	f1-C	f2-C	f3-C	f4-C	f5-C	f6-C	f7-C	f8-C	f9-C	f10-C	f11-C	f12-C	f13-C	f14-C	f15-C	f16-C	f17-C	f18-C	f19-C	f20-C	f21-C	f22-C	f23-C	f24-C	f25-C	f1-PO	f2-PO	f3-PO	f4-PO	f5-PO	f6-PO	f7-PO	f8-PO	f9-PO	f10-PO	f11-PO	f12-PO	f13-PO	f14-PO	f15-PO	f16-PO	f17-PO	f18-PO	f19-PO	f20-PO	f21-PO	f22-PO	f23-PO	f24-PO	f25-PO	label
0	6.563571	5.218949	3.049805	1.865306	1.835347	1.847321	1.295927	1.476782	1.581020	1.215593	0.884249	0.812913	1.016857	1.228776	0.699205	0.885908	0.783132	2.100794	4.669429	13.945848	24.054695	16.307320	10.754343	5.947487	2.421951	3.663504	3.069093	2.173351	1.614576	1.137822	1.170465	0.876294	1.179647	1.330423	0.850347	0.802822	0.814568	0.750993	0.691541	0.706926	0.783755	0.826655	1.282930	2.336160	7.259178	10.553705	7.778933	6.354040	3.437339	1.551205	4.576083	2.894511	2.078601	1.454941	1.185543	1.062370	0.836756	1.168269	1.042453	0.829707	0.850686	1.008244	0.830334	0.630146	0.527354	0.730469	0.687916	1.500483	3.591268	8.433888	12.503735	9.932653	7.790128	3.339286	1.804947	4.141203	2.661791	2.046841	1.427809	0.946644	0.971649	0.720028	0.922042	0.946869	0.834262	0.590012	0.671413	0.669496	0.621736	0.395491	0.714355	0.844734	1.338709	2.263675	6.400209	10.538869	5.724591	4.379153	2.240541	1.083646	6.123835	4.467297	3.082320	2.294817	1.855608	2.345501	2.139488	2.736754	2.744442	2.847328	2.147276	1.907946	1.941885	2.186922	1.790285	2.290137	2.503367	6.553236	15.197768	46.334747	70.978157	46.265961	33.684998	16.117390	7.076106	0
1	7.106468	5.865853	3.670119	2.525670	1.757730	1.454494	1.515261	1.003127	1.253562	1.062515	1.551892	1.684138	1.254109	1.109580	1.106682	1.220427	1.343397	2.722008	5.127455	9.961289	25.268732	22.986103	6.981561	3.383599	1.497205	4.370254	2.913755	1.434893	1.012069	1.244647	1.422224	0.929911	0.788371	0.935314	1.048800	1.213666	1.204103	0.736206	0.764350	0.921556	1.077512	1.345708	2.118414	3.410205	5.516843	9.368686	8.451645	3.491684	1.943778	1.120704	3.896196	3.054011	2.171311	1.933745	1.385818	1.145727	0.927655	0.853378	1.272307	1.171379	1.444009	1.338688	0.933652	1.316595	1.153059	1.396545	1.608738	2.311019	4.674912	8.707571	11.227053	9.061916	4.414537	2.216942	1.365601	5.722906	3.350319	1.659820	1.434130	1.312098	1.047988	0.640845	0.579390	0.694759	0.871206	1.261547	1.265671	0.776453	0.839956	0.831580	0.830842	0.957393	1.916355	3.143189	6.091893	9.496263	7.612154	3.898593	1.718512	0.821865	6.320039	6.259422	4.556241	3.373353	2.614609	3.181459	2.876030	1.974460	3.028919	2.893215	2.669268	3.205490	2.528575	2.038397	2.380801	3.271302	4.084576	7.877484	15.762299	29.033285	57.531002	52.772881	17.912193	9.421079	4.309249	1
2	5.645338	3.850049	3.841687	2.821426	1.472358	1.229566	1.446456	1.641682	1.443501	1.533185	1.134407	1.067169	0.899254	0.730645	0.899233	1.123670	1.277195	1.602292	3.551357	7.395586	23.833660	27.886475	9.032315	2.500861	1.354779	2.608359	1.992607	2.055577	1.683320	0.883149	1.205525	1.157706	0.984104	1.072439	1.467873	1.147233	0.879362	0.824711	0.705986	0.643295	1.121976	1.529175	1.628561	3.226027	5.045062	10.325281	12.217909	4.549701	1.659624	1.681398	4.004228	2.706271	2.220364	1.650101	0.911605	1.055124	1.151076	0.970878	1.082033	1.351030	0.960421	1.030163	1.012741	0.950353	1.157512	1.412505	1.684455	2.007699	4.720167	6.725069	12.848803	13.962900	6.111772	2.014341	1.462439	4.302824	3.044694	2.013773	1.535187	0.723679	1.070488	1.175701	1.164357	1.009519	0.940712	0.984593	0.900097	0.773316	0.862225	0.722842	0.813255	1.194329	2.424849	5.000575	5.816839	8.685947	10.772967	3.414757	1.109464	1.019996	5.314982	4.587234	4.649586	3.385375	1.746029	2.209270	2.602477	2.474704	2.460703	3.181598	2.305226	2.140144	2.026776	1.941422	1.815597	2.831618	4.103219	5.454243	12.017023	22.935608	63.837311	78.808357	27.835506	8.102716	5.853356	1
3	5.825416	4.379153	2.254036	1.856469	1.559564	1.404999	1.006046	1.145378	1.015998	1.085030	1.269364	0.826513	0.906712	1.130693	1.166102	0.764717	2.055008	3.320556	3.023809	4.883925	25.654896	25.890270	8.531851	3.268008	1.643061	3.902116	2.654109	1.844416	1.512796	1.586640	0.987245	1.400427	0.992402	0.830382	0.831070	0.709207	0.649797	0.715887	1.016638	1.177876	0.830008	1.260196	1.995956	2.288109	3.019880	11.081573	11.601875	5.812648	2.226065	1.333829	4.039240	2.456916	1.801400	1.487801	1.718468	1.255097	1.409776	1.111089	0.847709	0.913600	0.963879	0.680023	0.927635	0.924405	0.986989	0.841234	1.432885	1.716029	2.489958	3.845132	12.359094	15.000607	6.640305	2.115561	1.127271	4.048365	2.908583	1.863422	1.682972	1.680822	0.965806	1.072635	0.760299	0.901225	0.839097	0.835696	0.457415	0.629266	0.746714	0.876208	0.598069	1.240717	2.039274	2.207342	2.906249	10.020667	9.167999	3.648594	1.574333	0.705599	6.363115	5.147959	3.095874	2.930819	3.000298	1.882454	2.249208	2.633995	2.630122	2.679148	2.295885	1.383946	1.704238	2.595771	2.761475	2.367809	5.524978	8.288168	10.563636	17.088028	73.350342	79.993912	33.286079	10.554050	5.055670	1
4	5.427525	3.245119	2.194275	1.810796	2.151785	1.537857	1.395150	1.601232	2.045642	2.531717	2.201520	1.804507	1.212022	1.287893	1.567745	2.710736	3.678308	5.808706	10.444594	38.121349	40.583633	19.858059	4.776766	2.191746	1.192209	2.552024	2.194836	1.750992	1.476461	1.728028	1.332070	0.998824	1.048472	1.297141	1.440550	1.288863	1.452059	1.176997	1.003861	1.174765	1.878219	2.358376	3.832362	6.893865	15.878832	13.903761	8.807877	4.454870	1.654106	1.406330	4.581792	3.119571	2.102236	1.778868	1.996301	1.335411	1.149368	1.203947	1.306717	1.513003	1.161094	1.339023	1.030078	1.118636	1.403372	1.835237	3.129293	5.450362	7.567859	17.173563	14.939082	11.378635	5.119582	2.154449	1.744687	3.684063	2.636278	1.787825	1.296881	1.204362	0.997089	0.835766	0.875802	1.127335	1.245863	1.098258	1.845357	1.466477	1.265921	1.301269	1.942586	2.630375	6.579619	7.957227	13.837671	14.597376	13.247058	6.908531	2.004734	0.958466	4.762793	4.075068	3.263530	2.990970	3.848503	3.267094	2.837374	3.300380	4.067565	5.173850	3.497204	3.242993	2.820866	2.317641	2.503991	5.513987	9.193863	18.798601	31.487349	93.156487	95.078857	61.763027	20.199863	5.762176	5.454092	1
5	6.481053	3.140136	1.834569	2.070658	1.813956	1.660969	1.337258	1.053021	1.053009	1.130575	1.203415	1.106093	0.902843	0.976402	0.914155	1.430093	1.816641	3.414917	5.875501	6.779342	24.499355	19.155340	9.247709	4.360553	1.533730	4.062434	2.142581	1.421194	1.181379	1.252118	1.394301	1.158180	0.907338	1.113409	0.908130	0.914258	1.139736	1.005714	1.021604	0.711011	0.761775	0.980818	2.264972	3.438487	4.588935	10.259040	9.928383	5.184812	2.715486	1.641916	5.439908	2.677354	1.489190	1.511941	1.276190	1.172708	1.169048	0.884147	1.248014	1.184528	1.180209	1.019490	0.964411	0.979067	1.045240	1.352058	1.660021	2.506643	3.567224	5.434436	10.877089	11.752205	6.932767	3.532521	1.809434	4.446127	2.393024	1.323569	1.229195	1.334985	1.622099	1.167617	0.874490	1.307894	0.817221	1.033117	1.002920	0.698396	0.928571	0.642895	0.640129	1.001141	2.296771	2.982555	3.869442	6.886261	6.219142	4.488956	3.226719	1.812750	5.264829	3.469088	2.403425	2.668829	2.845136	2.580911	2.509265	1.902867	2.409238	1.863178	2.299460	2.809825	2.200168	2.074747	1.996679	2.578936	3.695454	9.853267	15.193131	19.842800	61.149544	60.227947	32.209854	16.844151	6.989918	1
6	5.977132	3.404175	2.534500	1.649823	1.426273	1.422183	1.628093	1.575967	1.950537	2.033227	1.482954	0.963641	0.918891	0.885805	1.040199	1.669682	3.663635	2.944945	4.180769	17.493334	44.619347	33.190647	6.348961	1.558184	1.649490	3.326223	2.144186	1.607120	1.343846	1.327802	1.183147	0.861769	0.887994	1.066758	1.174587	1.131617	0.879192	0.716470	0.646399	1.127504	1.638974	1.870462	2.413153	4.028671	8.078346	14.139254	11.595866	3.608258	1.281261	1.080699	3.524677	2.600114	1.717706	1.261696	1.298648	1.305978	1.089438	1.559422	1.558814	1.182137	1.000470	0.835523	0.610089	0.615221	1.047996	1.472695	2.213583	2.583060	4.539155	8.820228	16.577291	13.723910	4.264296	1.533680	1.183107	3.661635	2.550506	1.701580	1.374039	1.505264	1.224928	0.626090	1.171966	1.206328	1.006959	0.920994	0.504136	0.546518	0.714784	0.944155	1.169448	1.940537	3.369986	4.797884	9.820573	13.831742	9.902188	3.257880	1.568866	0.823531	5.309759	4.742834	3.959957	2.805272	2.587914	2.493015	2.190571	2.693058	3.090795	2.837010	2.528729	1.633591	1.513986	1.661532	2.580799	4.198932	7.101044	8.555116	14.840308	47.693932	101.768490	86.312515	23.884483	6.228777	4.861662	1
7	6.548693	4.519413	4.220215	3.573341	2.183829	1.777440	1.791289	1.204427	1.913306	1.879379	1.097257	0.996826	0.917542	0.947423	1.487709	1.724360	3.237389	3.474970	5.581635	9.011522	34.210236	27.662868	5.987046	2.777113	1.648717	2.803851	2.856565	2.717948	2.076202	1.768678	1.475826	1.307409	0.845496	0.815627	0.996436	1.008411	1.127802	0.797373	0.708543	1.078591	1.129434	1.480128	2.067911	4.463466	9.023745	15.982493	12.143299	4.344791	2.080870	1.439389	4.431253	3.675721	3.127463	2.328618	2.513121	1.547309	1.410247	1.427423	0.967951	1.374663	1.224174	1.082663	1.009091	1.222641	1.686140	1.686151	2.391971	3.039385	5.315371	10.809493	16.992727	13.103861	4.884930	2.096164	1.814873	2.385556	2.938675	3.091811	1.852130	1.296696	0.994968	0.667890	0.782285	0.830878	0.801165	0.934861	0.795698	0.685275	0.485276	0.841339	0.878886	1.453699	2.544112	5.707842	8.018855	16.742527	12.498936	3.177034	2.180302	1.822414	6.337083	6.254810	6.796540	5.348638	3.938848	3.345069	3.230646	2.450840	2.784669	3.889560	3.072565	2.266591	1.948554	1.510093	2.627926	3.717945	6.379502	7.420252	20.848679	38.948845	96.346741	77.848854	20.826080	9.374580	6.045331	1
8	4.400474	2.825733	1.986202	1.299412	1.402458	1.835921	1.169693	1.223791	1.124190	1.505562	1.373031	1.283809	1.518405	1.443064	0.958667	1.139041	1.211108	2.127244	5.704402	10.939195	33.933346	21.571917	7.211326	5.511249	2.084148	2.534487	2.065885	1.405351	1.258984	0.984332	1.131569	0.978504	1.402303	1.085983	1.342221	0.931932	0.786269	1.264995	0.964521	0.846224	0.708121	0.919199	2.106981	4.006731	6.525769	12.727263	9.479002	4.888270	2.675701	1.113502	3.848571	2.221501	1.620676	1.182889	1.035631	1.159038	1.073507	1.558455	1.172366	1.172473	0.932832	1.153583	1.087872	0.960852	1.022473	1.185450	1.481652	2.069560	4.686768	7.196881	13.739634	9.267789	4.612847	2.748652	1.497290	4.050717	2.097500	0.972147	1.428532	1.158810	1.115509	0.977549	1.440406	1.043172	1.373917	0.969766	0.575666	0.783164	0.600995	0.516189	0.568690	1.157549	2.466685	4.471868	6.116581	11.609340	7.714385	4.533068	2.727925	1.200072	5.129218	3.934638	2.724906	1.901329	2.229983	2.583431	2.035813	2.644156	2.402589	3.583161	2.421777	1.814194	2.186708	2.079428	1.621276	2.415297	2.687982	6.933627	17.287815	29.797371	77.778229	54.429901	21.817911	14.628593	6.266222	1
9	5.271265	3.960141	2.555062	1.626180	1.301536	1.534220	1.561467	1.241041	1.400270	1.838159	1.484781	1.175208	1.268717	1.190853	1.738567	1.969446	1.845911	2.560166	4.525519	5.811416	16.431160	21.826042	8.185096	3.164586	2.467710	3.062671	2.238725	1.510420	1.358135	1.236413	1.233235	1.152440	1.047324	1.123449	1.103596	0.806929	1.059013	0.898391	0.718703	1.000959	1.209502	1.653174	2.167481	3.258148	4.238212	9.717659	10.014038	5.594355	2.623246	1.910861	4.714748	2.814901	1.868458	1.336997	1.101537	1.474485	1.967315	1.423409	1.316015	1.318618	1.029710	0.973542	1.024044	1.224928	1.448242	1.558344	1.926670	2.977589	4.759735	5.182094	12.538350	13.194104	5.649567	2.547205	2.051527	5.002839	3.028441	1.154381	1.058184	0.954817	1.153257	1.145812	1.030836	0.890992	1.242522	1.019659	1.127967	0.884916	0.630387	0.952894	1.121992	1.629218	2.130073	3.551570	4.495875	10.581645	9.463018	4.234572	2.107805	1.921786	4.852959	4.141069	2.947386	2.319951	2.124017	2.536024	2.470210	2.043503	2.507316	3.426025	2.612882	1.895195	2.130137	1.988467	2.943522	3.641964	5.074918	8.743999	13.785311	16.324369	48.199261	65.302101	29.700838	11.514667	8.572239	1
10	5.540150	3.090554	2.018193	0.933150	0.637777	0.452002	0.404917	0.495195	0.532654	0.677036	0.754599	0.733305	1.154826	1.321398	1.492004	1.067014	0.848308	1.480762	3.933686	2.613607	1.010697	0.548482	0.326763	0.237602	0.183827	3.397114	2.143236	1.195710	0.815975	0.893830	0.659823	0.846596	0.764176	0.560833	0.584995	0.453958	0.558395	0.813515	0.958999	1.458759	1.157322	0.953797	1.786720	6.056314	4.120083	1.191445	0.535802	0.421441	0.305077	0.242821	2.160354	1.106576	0.746096	0.583836	0.418330	0.335130	0.393418	0.439716	0.348602	0.368084	0.422172	0.497173	0.538428	0.701951	0.798360	0.794526	0.780905	1.224929	3.538782	2.397343	0.703490	0.571707	0.451980	0.314966	0.166638	1.535289	0.810975	0.439794	0.332496	0.353301	0.284661	0.267273	0.275619	0.255109	0.213834	0.250094	0.297905	0.168685	0.257487	0.398227	0.454108	0.437740	0.649167	2.484165	1.660957	0.446992	0.213326	0.160746	0.076565	0.095893	2.854582	2.022012	1.360774	0.705742	0.613097	0.402946	0.410992	0.420242	0.460339	0.601948	0.427108	0.404465	0.728834	0.878004	1.302944	0.937674	0.967600	2.243126	7.911468	5.297451	1.446578	0.487840	0.321139	0.253212	0.203761	0
11	2.902404	1.307604	0.800657	0.667843	0.605198	0.662919	0.371883	0.482090	0.552557	0.452228	0.513404	0.535822	0.618423	0.597458	0.500083	0.696850	0.746141	0.668842	2.029910	3.629331	2.405832	0.954480	0.317848	0.232937	0.142755	2.770122	1.455743	0.969645	0.654653	0.498942	0.485435	0.333538	0.687184	0.741790	0.433492	0.566509	0.667411	0.425455	0.445242	0.632047	0.921462	0.903238	0.821450	2.755212	4.558518	2.700182	0.860686	0.273062	0.331639	0.304727	2.257227	0.703344	0.635905	0.561225	0.426998	0.413655	0.300478	0.386553	0.397399	0.297786	0.288721	0.367910	0.428250	0.279117	0.334495	0.660697	0.702047	0.655736	1.527559	2.452692	1.822896	0.843172	0.412186	0.243141	0.161802	1.134122	0.452790	0.426051	0.235157	0.183684	0.191206	0.153160	0.250356	0.231226	0.181848	0.151346	0.177518	0.122905	0.175361	0.225543	0.414685	0.381468	0.288002	1.046556	1.800668	0.945164	0.280692	0.103222	0.093576	0.069417	1.668247	0.844564	0.622868	0.592708	0.486086	0.351221	0.229269	0.392133	0.476027	0.334600	0.378303	0.426305	0.390514	0.430967	0.495005	0.688487	0.820023	1.041726	3.537361	5.285998	3.299338	1.102811	0.255267	0.196056	0.148027	1
12	6.504219	2.710809	2.258221	1.414209	0.410901	0.423112	0.432000	0.524639	0.466444	1.005254	1.405419	0.848664	0.991219	1.216515	1.209367	1.574540	1.730500	2.066922	3.707594	4.904940	1.844885	0.805263	0.475455	0.385918	0.228603	5.463698	3.096947	2.441771	1.323234	0.840068	0.606334	0.577676	0.515674	0.574220	0.725108	1.013007	0.895735	1.283851	1.102863	1.737999	2.247565	1.481983	1.677082	4.800088	5.928533	2.072020	0.901507	0.669766	0.467818	0.273145	2.290152	1.455730	1.285220	0.623714	0.479765	0.508076	0.425292	0.463362	0.356718	0.461634	0.603761	0.570507	0.786390	0.814661	0.986269	1.204087	0.949337	1.062847	2.436499	3.180438	1.449948	0.628823	0.521090	0.433996	0.177966	1.956381	1.485687	0.919899	0.361391	0.374465	0.248429	0.199201	0.147975	0.144591	0.187043	0.301234	0.364151	0.431623	0.442373	0.728952	0.772701	0.647593	0.732313	1.815206	2.443241	0.898570	0.322405	0.182968	0.127375	0.076560	3.559895	2.305846	2.155700	1.129042	0.482023	0.364911	0.361577	0.416513	0.392640	0.534338	0.790114	0.627257	0.640323	0.845248	1.378935	1.705687	1.556040	2.283362	6.675550	8.045681	2.438554	0.837440	0.437834	0.311747	0.187102	1
13	2.487236	1.439516	1.577201	1.020079	0.760848	0.773233	0.626487	0.552451	0.671958	0.687424	0.556172	0.674917	0.833967	0.802506	0.659658	0.651953	1.105599	1.500318	3.157489	3.008418	1.826734	0.950244	0.636550	0.265335	0.232813	2.568852	1.785986	1.729391	0.948525	0.645747	0.531280	0.514457	0.400626	0.477362	0.761391	0.881850	0.818026	0.592432	0.631863	0.848684	0.632913	1.264357	1.763671	3.836398	4.219982	1.472076	0.737574	0.471498	0.307121	0.183799	1.611101	0.967344	0.932744	0.710525	0.652649	0.503999	0.485227	0.446327	0.499205	0.481791	0.570062	0.440367	0.386347	0.421329	0.458332	0.496745	0.759795	1.066115	2.674546	3.193920	1.508287	0.505226	0.301930	0.222901	0.174893	0.663508	0.518423	0.622009	0.468933	0.354105	0.203919	0.197313	0.171157	0.191678	0.246312	0.327653	0.236051	0.208967	0.275177	0.312731	0.229738	0.453539	0.848956	2.211444	2.058686	0.781893	0.331394	0.128156	0.116288	0.073601	1.776852	1.165522	1.312646	0.697160	0.523927	0.474116	0.466097	0.373221	0.523703	0.690373	0.643920	0.607138	0.504114	0.582129	0.732520	0.620386	1.330485	2.202889	5.405133	5.288417	2.131996	0.966488	0.502050	0.258997	0.196970	1
14	2.499131	1.177657	0.927153	0.950739	0.626692	0.727806	0.794336	0.584832	0.651950	0.791156	0.516564	0.763592	0.849295	0.916991	0.956498	0.743391	0.627661	0.976353	3.524917	6.164290	1.804286	1.265489	0.690610	0.366337	0.263137	3.120067	1.746898	1.079904	0.895852	0.433727	0.585903	0.831333	0.969794	0.694989	0.795828	0.526675	0.723503	0.867163	0.898088	0.898147	1.044499	0.988325	1.163176	4.467923	6.094364	1.624553	1.270115	0.805187	0.372855	0.238212	1.651166	1.051318	0.745284	0.480896	0.409348	0.511275	0.584404	0.529658	0.324460	0.332906	0.317751	0.485560	0.630469	0.621105	0.632110	0.730086	0.805306	0.958658	2.295982	3.358211	1.464107	1.565954	0.921080	0.286582	0.151777	1.382510	0.698209	0.366133	0.332552	0.264078	0.183322	0.225317	0.334048	0.268242	0.325956	0.211964	0.200722	0.259402	0.295979	0.439361	0.732662	0.408846	0.427962	1.365178	1.749057	0.505652	0.366906	0.257887	0.119511	0.085608	2.067716	1.359006	1.017454	0.919690	0.366238	0.448788	0.568805	0.620504	0.658024	0.797002	0.406407	0.535774	0.603394	0.625891	0.835141	1.023480	0.926619	1.298067	5.879684	8.666868	2.273080	1.178578	0.675105	0.289349	0.190139	1
15	2.706493	1.443828	1.350562	1.032386	0.858683	0.647472	0.798378	0.723954	0.946662	0.757643	0.999406	1.755328	0.899568	1.123147	1.477610	1.286038	1.025315	1.554039	4.857675	7.834015	3.096786	1.085263	0.569224	0.380918	0.312885	2.077707	1.958646	2.269222	1.474547	0.832699	0.604215	0.935227	0.606775	0.880366	1.019814	0.889434	1.228040	0.893619	1.010702	1.352243	1.259439	1.127555	1.643302	6.681853	9.931477	2.976509	1.036062	0.567719	0.454500	0.361135	1.585019	1.185707	1.328077	0.744158	0.514719	0.549902	0.605729	0.436198	0.469014	0.615350	0.523329	0.747784	0.526980	0.473152	0.648773	0.764767	0.803963	1.387930	3.667703	4.875229	1.838030	0.732621	0.624142	0.323067	0.203597	1.139598	0.716992	0.945628	0.476724	0.257082	0.233501	0.465335	0.322554	0.260078	0.331638	0.258508	0.312386	0.282291	0.283268	0.423144	0.508981	0.478524	0.675855	2.748344	3.861779	1.065714	0.441429	0.208492	0.160323	0.090204	1.701640	1.473763	1.974860	1.368025	0.829651	0.488611	0.583811	0.492495	0.817746	0.790819	0.708857	1.171679	0.670986	0.862782	1.203953	1.120801	1.083278	1.831197	8.745273	11.861046	3.264477	0.998109	0.426914	0.278445	0.255779	1
16	3.157382	1.413209	1.217496	0.894466	0.483632	0.474033	0.570692	0.778974	1.031830	0.844955	0.846145	0.727473	1.093899	1.051350	0.779532	0.906649	1.071982	1.287159	4.385722	5.345548	2.572059	0.762509	0.613470	0.377478	0.252352	3.052116	1.562377	0.961358	0.745059	0.771298	0.726232	0.787279	0.989421	0.981628	0.565094	0.728459	0.784453	1.414232	1.235757	0.954914	1.324536	1.462393	2.105453	7.019000	5.901556	2.624680	0.639334	0.505901	0.354054	0.245958	1.963725	0.959915	0.718865	0.573798	0.406257	0.304984	0.346131	0.481991	0.605319	0.443889	0.487632	0.450895	0.587897	0.520608	0.473138	0.956438	1.311656	1.642838	3.359135	3.433696	1.761823	0.598563	0.637902	0.422553	0.242596	1.857730	0.561136	0.227532	0.165619	0.250941	0.316721	0.388787	0.289787	0.226292	0.175736	0.254309	0.317282	0.370307	0.462206	0.313758	0.433147	0.522920	0.718049	2.751821	2.245745	0.862830	0.163144	0.157403	0.124970	0.081509	1.973250	1.299677	0.968865	0.739500	0.614637	0.572628	0.575033	0.620094	0.849099	0.566174	0.612910	0.632088	1.049505	0.872615	0.877959	1.166508	1.419923	2.379554	8.957398	7.977750	3.373874	0.672494	0.419172	0.284942	0.205869	1
17	4.093120	1.520644	0.833136	0.941199	0.607955	0.589274	0.579221	0.493426	0.553222	0.683611	0.736905	0.715773	1.084809	1.250808	1.129468	1.181026	0.646531	1.249112	1.725797	1.591862	1.256199	1.469493	0.726516	0.395191	0.315322	3.494206	0.991750	0.805061	0.912916	0.667239	0.626168	0.643385	0.422093	0.483979	0.491365	0.479722	0.532522	0.800415	0.719952	0.773779	0.588640	0.673913	1.544223	2.816223	2.647996	1.396599	0.976190	0.639769	0.362356	0.244219	2.108166	0.843705	0.585688	0.504949	0.417155	0.466783	0.331891	0.240080	0.286225	0.373841	0.477066	0.348976	0.377044	0.335080	0.463333	0.667454	0.590764	0.835183	1.549923	1.421044	0.969982	0.973414	0.536303	0.281524	0.221134	1.067533	0.425602	0.293675	0.271271	0.264600	0.268349	0.252989	0.162951	0.134881	0.183948	0.124105	0.141646	0.193841	0.166171	0.217850	0.214788	0.282168	0.560678	1.437046	1.247824	0.440212	0.312792	0.196254	0.126462	0.080355	1.921663	1.015792	0.720185	0.765150	0.494270	0.463315	0.435263	0.366637	0.425418	0.457690	0.494910	0.487078	0.693127	0.737610	0.885302	0.741228	0.737227	1.991964	3.553725	3.027160	1.540168	1.267163	0.611083	0.312116	0.214146	1
18	3.352311	1.743547	1.278361	0.789084	0.939441	0.845813	0.816430	0.930290	0.877484	0.921537	1.254369	1.397150	1.428238	1.693948	1.537014	1.674111	1.170039	1.860775	9.465800	12.783307	2.354671	0.672576	0.500303	0.337010	0.309404	3.124632	2.008881	1.388921	0.889857	0.718235	0.910245	0.955990	0.685545	0.700396	0.823283	1.155161	1.279116	1.600967	1.929428	1.646742	1.397446	1.283029	1.744763	8.469608	10.899537	2.696323	0.562953	0.552564	0.364842	0.256036	1.744071	1.030084	0.829876	0.520171	0.473733	0.545014	0.569286	0.519677	0.632839	0.715123	0.698955	0.793569	0.879166	1.323957	1.162028	0.934653	1.010026	1.544780	5.589171	8.067873	1.823892	0.556149	0.698308	0.417250	0.220524	1.098312	0.653916	0.640446	0.304437	0.186285	0.276510	0.243144	0.156264	0.253713	0.278107	0.284617	0.386673	0.496497	0.553453	0.488153	0.451733	0.496653	0.661321	3.318704	4.163748	0.775773	0.142962	0.196480	0.138049	0.090822	2.261589	1.522307	1.194704	0.935138	0.824528	0.780830	0.721326	0.669044	0.795387	0.856205	1.079770	1.201053	1.133002	1.580193	1.325748	1.296898	1.251331	2.250909	11.838403	14.926291	2.722847	0.600639	0.489281	0.275682	0.204682	1
19	6.594454	2.821067	1.611422	1.091008	0.738797	0.700309	0.906979	0.686952	0.871589	0.722948	0.813029	1.244523	1.382156	1.461157	1.228974	1.153183	1.496682	1.320609	5.741862	7.593173	2.346715	1.020326	0.723528	0.526942	0.384153	3.899606	2.614410	1.360994	0.895410	0.633492	0.451619	0.711426	0.559052	0.818641	0.745316	0.711080	1.130717	1.188974	1.300189	1.042930	1.530095	2.360758	2.031071	4.747500	5.845414	1.933795	1.242605	0.878659	0.489854	0.418408	2.707133	1.460436	0.884381	0.454027	0.478820	0.488484	0.660714	0.497735	0.435451	0.440410	0.491027	0.681541	0.674795	0.609857	0.641675	0.936380	1.214076	1.185522	2.726904	3.010720	1.099813	0.715126	0.579604	0.359371	0.260138	1.094980	0.810786	0.636308	0.370728	0.306753	0.230418	0.185747	0.134474	0.255068	0.230065	0.211420	0.416115	0.420150	0.476692	0.459677	0.618372	0.786445	0.808313	2.175858	2.216876	0.600160	0.354830	0.233308	0.168854	0.111263	3.092360	2.285372	1.351299	0.811293	0.541564	0.394506	0.576981	0.396079	0.738643	0.706782	0.649507	0.885581	0.960576	1.077759	0.887224	1.338615	2.526599	2.295883	7.270337	9.221198	2.745305	1.183273	0.769475	0.442229	0.340460	1
20	7.404556	3.680870	2.958437	3.719976	3.011786	3.294215	3.320111	8.758327	12.723364	5.896781	2.639693	2.733134	2.171217	1.502289	1.905321	3.359633	5.162352	16.333221	47.804932	21.832705	5.949067	2.564674	0.715093	0.373338	0.534171	5.689558	2.891008	2.738876	3.450386	2.429032	2.286737	3.389123	7.828780	8.675612	4.104453	2.524024	3.262136	2.888938	2.179515	2.689750	4.032264	5.778693	12.710095	25.834940	12.444290	3.997457	1.662609	0.699112	0.670170	0.594609	5.144881	2.496780	2.140176	2.693537	2.127862	2.198009	3.425273	7.117631	9.015619	4.508224	2.401851	2.254617	2.213494	1.976391	1.790729	2.525955	3.480919	10.985908	33.809578	17.792917	5.371440	1.659828	0.950812	0.834975	0.641076	9.279324	4.632252	3.743624	3.366153	2.561269	2.658541	3.625490	7.963244	9.436162	5.553454	3.794284	3.394193	3.395189	2.225730	2.059186	3.467614	4.913398	16.792957	55.931293	36.861290	7.601811	2.283324	0.730339	0.499079	0.648691	5.693117	3.714685	3.683955	4.423019	3.092546	3.472098	4.779472	11.960680	15.213515	7.023909	3.041706	3.371668	3.140667	2.460292	2.428277	4.026754	6.344469	22.424854	67.048233	29.836176	6.226585	2.067539	0.854400	0.583913	0.665683	0
21	5.385716	4.919188	8.577614	6.906911	3.160691	2.864680	4.386667	11.548931	8.370485	5.218397	3.897581	2.805455	2.580982	3.686243	3.307505	4.779960	8.557730	47.423298	78.389244	19.962215	3.856885	2.026060	2.055078	1.193671	0.837350	4.662528	4.106412	6.372695	5.485542	3.912782	2.872150	5.021421	12.521502	6.898109	4.369906	3.761982	3.323886	3.242181	3.966555	3.561055	4.630235	6.330123	21.171457	34.465599	9.987172	2.488655	1.339606	1.412353	0.989926	0.927727	4.508396	4.530847	6.751830	5.279427	3.134913	2.810575	4.228674	8.901962	6.933051	5.996880	4.111615	3.234654	3.032320	4.066543	3.435849	3.750454	6.021656	24.431536	45.390743	14.552032	4.286712	2.044280	1.495090	0.962861	0.984685	7.851584	6.161496	9.627949	7.296128	4.249671	2.485708	3.668733	9.296202	8.387830	6.664452	4.226841	3.070633	3.770712	4.165095	3.372105	4.608511	7.027700	31.476250	64.626205	24.573982	4.247332	2.099176	1.859654	1.329184	0.970491	5.186744	5.353741	10.320778	8.130285	4.255516	4.044217	6.665437	15.492460	10.473836	7.868081	5.126401	3.963962	3.854987	5.439281	4.720867	5.404309	9.219976	51.167057	92.375595	23.529364	3.993707	2.205763	2.191656	1.384358	1.062272	1
22	6.535532	6.157684	7.756414	4.399813	2.970226	3.761811	4.216240	6.386446	8.284849	6.333727	5.318600	4.965253	5.019751	5.892276	6.838257	6.782275	8.997962	48.437874	106.923390	26.798523	5.063389	1.885604	1.655871	0.777333	0.772202	5.547879	5.744375	6.952333	4.402119	3.505164	3.173287	4.082412	6.361419	6.843984	6.085485	4.714688	3.257603	5.021738	4.696145	4.992069	4.057792	5.055703	22.385723	46.957867	10.431808	2.330060	1.410607	1.369571	0.959127	0.679301	5.240948	5.750662	6.503699	4.387755	3.532672	4.229514	4.723914	6.584882	7.824931	7.180811	5.265870	4.990491	4.475398	3.757660	5.022122	5.836698	6.534780	31.250204	61.757713	15.260574	3.968810	1.787131	1.490711	1.286120	1.361771	8.830647	7.116405	8.540807	5.633544	3.874594	4.319287	4.243969	5.176215	8.910332	7.931809	5.355638	4.352991	5.057843	4.273993	5.297523	5.918984	6.659359	35.361465	86.350021	23.092192	6.539772	2.290369	1.080811	0.747650	0.698663	4.707177	7.082040	9.781990	5.997478	4.274567	5.422915	6.631710	9.351065	10.727847	8.851871	6.818298	6.519874	7.029492	6.291332	8.225371	8.329482	10.748889	57.605152	120.662510	24.391491	3.824905	1.807233	1.376105	0.877883	0.781648	1
23	5.310919	3.468067	4.790256	4.563872	3.453330	2.960412	4.978153	5.187681	4.366084	6.089180	4.475863	3.475238	4.062844	3.128384	4.826706	4.953524	9.776623	61.341843	84.475258	16.134941	2.255462	1.435861	0.950031	0.641472	0.859451	4.543410	3.109838	5.609140	4.629063	3.421631	2.740972	4.308780	6.860774	5.607844	4.099621	2.670636	3.318069	4.088035	2.954299	2.982323	4.135631	5.875694	26.133362	36.118484	8.363278	1.766717	1.713312	1.436306	0.742075	0.611260	5.393792	3.627042	4.653195	3.902379	2.122040	2.063759	3.926871	5.580781	6.609602	5.194705	3.957923	3.641911	3.735593	2.779262	3.175924	3.686703	5.726488	32.303097	49.911972	12.065797	2.113647	1.735811	1.641662	1.191064	1.115224	6.154633	4.673812	8.146583	6.405686	3.563046	2.881015	5.199701	6.495285	5.711857	5.227939	3.661162	3.340539	3.205863	3.075115	3.720663	4.183744	6.437526	39.665581	62.054680	19.257872	3.128269	1.668090	1.235515	1.012562	0.798651	4.470841	3.609721	6.807285	5.740959	3.537417	3.888523	6.893948	9.164472	8.405203	7.619088	6.000332	4.970602	5.361402	4.345490	5.649360	6.080463	10.203978	67.246155	99.624931	21.292709	2.620243	1.872952	1.306551	0.799655	0.983052	1
24	10.377501	7.084032	4.513622	5.111720	4.070040	3.185612	5.202882	9.568634	9.488035	4.541996	4.120071	4.659471	7.507012	8.874672	8.570350	5.246399	7.370882	48.843899	79.256859	14.218609	2.760283	1.498880	1.570654	1.090539	0.696259	6.108139	5.393618	4.825580	5.638963	3.797212	2.589472	5.997890	11.813110	8.689090	4.654022	4.672709	4.407047	5.126651	6.497179	6.589652	5.038615	6.675924	25.995214	36.711735	6.920103	2.292044	1.445901	1.112173	0.775821	0.617047	5.851376	6.041684	5.126507	5.726399	3.689233	2.872670	4.816288	8.109328	7.877345	4.621846	4.155969	4.967225	7.117678	6.106534	5.955926	3.297186	5.769616	32.938713	49.345856	9.340192	3.493896	2.216893	1.445637	1.149390	0.793146	6.662637	5.749844	5.394767	7.039652	5.340910	2.395079	4.490530	11.139504	9.246203	6.177469	4.864589	3.791708	6.781451	6.596661	7.937277	5.147934	7.298161	46.579399	75.647079	14.506627	3.976247	2.129728	1.973714	1.188766	0.770350	7.009489	6.808390	6.092795	7.343045	5.130106	3.748787	6.688034	14.068696	13.226796	6.459300	5.851252	6.482820	10.006499	10.633663	9.900760	6.292421	10.338313	64.496284	99.312859	14.871268	2.731706	1.598723	1.462316	0.962282	0.742597	1
25	8.330341	6.543579	6.697051	4.993723	4.857743	5.448010	5.972238	7.754193	8.098502	4.437121	4.745663	7.670741	7.262125	4.047639	5.016559	6.037572	7.952039	55.546593	93.862549	18.106853	5.093777	1.797578	1.282056	1.054645	0.899342	5.540948	5.111618	5.862021	4.447346	3.239077	3.251391	6.898116	7.853537	5.357781	3.452020	3.936108	5.246304	4.839200	3.415965	4.260960	4.471543	6.171873	25.160753	39.996052	10.099655	3.421577	1.729116	1.131382	1.026177	0.780338	5.922350	5.304830	5.548980	4.558817	4.785726	4.332024	6.093844	6.519511	6.932034	4.753419	4.826663	6.874326	5.030707	3.847722	4.486511	5.156625	6.404292	31.770350	55.569824	13.607161	4.160280	1.882256	1.864526	1.091438	0.814457	7.504959	7.782750	8.486722	4.942376	4.115506	4.388437	5.787372	7.781442	10.079463	6.361998	4.542618	6.503448	6.333704	4.964268	6.688215	5.695877	6.999722	39.344212	75.998840	20.840151	6.433085	1.813097	1.336607	1.196721	1.035502	7.078846	7.172573	7.958681	6.359100	5.569325	6.388550	8.875030	10.190446	9.422193	6.189017	5.686612	9.236316	8.742513	5.206559	6.394270	7.689764	8.877745	61.704727	108.061970	19.068649	5.040477	1.745499	1.475839	1.132991	0.873532	1
26	7.703055	7.070810	6.460716	7.912210	7.039597	6.904835	9.013117	10.233867	7.795015	6.932983	5.991081	8.488300	7.460778	4.729825	4.391142	5.015166	9.230315	66.171577	100.647380	10.566132	3.120330	1.501213	1.012757	0.749603	0.872491	6.457845	6.681419	6.457221	7.024866	6.235859	6.963755	8.627638	9.853659	8.638625	7.723638	6.064206	5.973020	6.279006	4.831927	3.040861	2.522320	4.478421	30.116852	47.195431	5.966193	2.507118	1.629423	1.071310	0.940409	0.826067	7.065402	6.008163	5.652645	7.403941	6.970164	6.639764	6.113999	8.078889	7.560706	6.000316	5.992419	7.759392	6.781068	4.358380	3.707841	3.497879	5.387979	39.686703	56.972889	8.413953	3.478259	1.608650	1.184424	1.109203	1.077033	7.964427	9.260192	9.516329	9.819827	6.245486	5.507563	5.986700	9.592225	9.022307	6.766992	6.348861	6.571071	7.726443	5.428419	5.540744	5.605121	5.659713	50.962440	78.136948	10.513506	4.401887	2.322913	1.402045	0.935641	0.713007	7.807788	8.486669	8.244174	10.751757	8.845459	8.682140	10.610972	13.146191	10.330360	8.649972	8.189517	10.001671	9.907892	6.808129	5.540805	5.444767	9.536276	81.814476	119.944060	12.838080	3.903738	1.845278	1.218120	1.018838	1.021126	1
27	9.906985	9.011810	8.115727	8.423458	6.500381	5.614459	8.482864	15.529570	10.897723	9.156615	7.113888	7.831369	8.786875	7.526581	8.892779	11.680190	13.840084	53.595455	113.199420	34.574284	7.286897	4.663290	1.609853	1.458724	1.098324	7.423631	8.168510	7.666738	11.099867	7.341151	6.574609	9.600575	12.492249	7.634355	6.610233	6.435481	5.678018	7.821228	8.347789	6.194258	6.679790	7.624388	25.657812	49.484154	15.666517	3.894579	2.774904	1.890319	1.376560	0.721141	5.951738	6.715999	6.550162	8.503921	6.705326	5.925905	7.066930	12.131557	10.248460	9.217199	5.383437	6.545760	8.111845	5.474532	5.617020	7.720381	8.907541	30.054417	65.088524	22.232079	5.611769	3.260436	1.671618	1.798031	1.495747	7.561659	7.569908	8.558106	10.804554	7.141304	6.409371	8.984028	14.752769	11.930923	9.165282	6.168806	7.362671	9.217050	7.193833	6.847315	9.120000	10.166189	33.762634	85.918991	41.525082	8.183661	3.754668	1.368095	0.934974	0.960735	7.632975	9.672081	9.892469	10.728014	8.596220	7.568364	11.206474	19.699989	13.521053	11.881545	8.426090	9.108032	11.251433	8.345089	8.045683	11.808920	14.978025	57.726128	124.548600	35.975140	6.799021	4.483043	1.552533	1.454233	1.237261	1
28	8.093306	5.639049	5.022012	4.651377	4.546170	5.025071	9.829739	10.254506	14.981661	11.033735	6.068525	5.763089	7.564654	8.905012	4.849700	4.584195	7.125857	38.941193	119.519450	29.940729	6.682446	1.904673	1.314007	0.931127	0.669158	5.091820	7.269713	7.725492	5.010524	4.588809	4.901693	7.130801	10.213565	13.430417	9.034636	5.577098	5.567125	5.652183	6.761294	5.021701	4.364244	4.922616	17.875629	42.791428	12.338014	4.276295	2.123400	1.521962	0.802372	0.631450	8.222130	6.151038	7.294586	7.530538	5.975195	5.180078	7.112426	7.992859	11.159659	9.035098	7.009159	5.329876	5.771459	6.206970	4.742342	4.700550	6.094805	22.858273	63.078400	18.687326	5.488121	2.523039	2.097375	1.015747	0.843966	6.794554	7.867773	9.281000	6.299406	4.974788	5.470746	7.365474	8.621206	11.174250	7.794082	5.710246	5.367800	6.538447	7.283768	4.935058	4.529133	7.164049	25.579336	69.532104	25.395176	10.023599	3.920439	2.189815	1.062729	0.806966	7.414157	7.416265	8.236758	7.355045	6.372084	6.759511	11.856764	13.652046	18.568872	13.685248	8.880046	7.546856	9.045296	10.874989	6.238141	5.677663	8.913701	42.510223	124.675740	31.121908	7.183721	2.530635	1.836509	1.083932	0.712547	1
29	11.082429	15.191614	15.858020	13.014374	10.198432	9.635305	9.044727	8.854509	9.948845	11.436628	8.012613	6.338630	9.308723	10.848383	10.169788	9.139582	13.186529	52.686108	102.190290	32.619648	6.013007	3.783330	1.777523	0.798135	0.629922	9.596906	12.310314	12.248006	8.440698	7.310163	7.353426	7.530007	6.375394	8.939696	7.198805	6.318319	6.266471	5.717725	6.449884	7.213830	5.366707	8.401106	24.535769	42.629139	16.018541	4.437357	2.316314	1.385542	0.857536	0.481738	10.250408	12.821988	12.997778	9.469717	7.202235	5.852672	5.870763	6.925366	7.578660	7.806320	6.152320	5.722067	6.576559	6.501184	7.124026	6.264202	8.284002	27.729208	55.597179	20.906534	7.009724	3.773030	1.703880	0.940973	0.597254	10.156580	15.507960	13.555676	12.683839	10.983188	6.500209	5.791139	8.428128	11.620444	8.477910	6.474057	5.561956	5.996783	6.248427	7.440786	5.965568	8.450701	30.364697	72.152939	28.210827	6.018916	3.515956	1.695259	1.177642	1.124118	8.651875	17.843246	19.461369	15.617816	12.706240	10.764644	10.607952	10.967900	13.090324	13.245263	8.986157	8.187949	9.769968	10.579857	10.388315	9.488978	14.605476	54.703587	109.319590	32.867775	6.111577	3.773603	1.772175	0.899636	0.649192	1
30	5.194055	2.250538	1.752779	1.766805	2.130169	2.526149	2.049918	3.021815	2.045260	1.760898	1.878834	1.485466	1.318188	1.099991	1.111159	2.159392	2.722733	5.679788	49.949421	74.385185	10.596630	1.782635	1.008106	0.867037	0.677902	12.829686	4.713079	2.957021	2.240536	1.963635	2.111099	2.162028	2.067905	1.904405	1.548411	1.590756	1.654070	1.557171	1.382556	1.461123	1.563533	2.223707	3.403745	16.821486	23.729614	3.547077	1.123431	0.920057	0.531715	0.406836	2.803388	1.724207	1.189390	1.396798	1.439291	1.600301	1.742463	1.711898	1.346157	1.307010	1.135861	1.168334	0.822250	0.834624	0.931056	1.342683	2.050622	2.875411	13.908195	20.647732	3.152750	1.015957	1.005835	0.809696	0.780347	2.426964	2.157508	1.533775	1.623133	1.699113	1.676142	2.003655	2.256599	1.821207	1.611537	1.768656	1.324263	0.979709	0.938482	1.311192	1.827542	3.061774	4.355505	17.270847	26.823429	4.115963	1.364814	1.017128	0.522674	0.501185	4.993115	2.635798	2.590277	3.304226	4.134093	5.312419	5.512470	6.281776	3.531657	3.819890	4.210812	3.170594	2.921606	2.429106	1.923050	3.792244	4.812841	7.802138	75.152412	124.227130	17.806538	3.270781	2.305828	1.095752	0.881692	0
31	4.713806	2.753494	1.873573	1.557446	2.303675	2.799354	2.914047	2.307140	2.726849	2.603541	2.385392	1.543532	1.077650	1.211103	1.227264	2.003264	2.198925	4.183745	26.513140	51.804249	11.365935	2.698250	0.911866	0.585569	0.766826	12.631887	6.411925	4.349653	2.968081	2.386400	2.495214	1.899522	2.002778	2.397494	1.933002	1.784291	1.445029	1.468926	1.364287	1.214369	1.559639	1.519510	2.291702	8.883566	17.434500	4.448381	1.229649	0.661142	0.643825	0.767773	2.991075	1.806501	1.608922	1.427475	1.674345	1.667461	1.247828	1.351734	1.679527	1.433334	1.584608	1.236935	1.073541	1.177434	0.829900	1.342748	1.448330	1.694397	7.903231	15.081291	4.002904	1.307604	1.064567	1.345473	1.872433	3.127100	1.693816	2.059518	1.815784	1.317555	1.497617	1.145256	1.285610	1.561499	1.536055	1.667923	1.046370	0.754567	1.109748	1.212523	1.351858	1.369347	2.593159	15.038684	26.665751	5.810605	1.698204	0.756533	0.605193	0.448817	4.213427	3.515212	2.622467	2.444924	4.543937	5.937199	4.996547	4.201305	5.190628	5.098809	4.572055	2.993123	2.197004	2.378139	2.003165	3.573439	3.313333	4.882965	35.163380	75.287201	19.170588	4.217266	1.522708	0.931192	1.055076	1
32	4.455434	2.776899	2.584200	2.626793	2.531660	2.685441	2.461445	2.160547	3.360936	3.796628	2.150992	1.357349	1.035110	1.194230	1.139212	2.016581	2.418798	5.091268	41.931656	72.446510	17.688923	3.015684	1.344984	0.978875	0.811767	6.725345	3.151172	1.722479	1.759596	1.725837	1.442644	2.044194	2.050406	2.529833	2.580155	1.582437	1.286397	1.294130	1.343156	1.516251	1.190336	2.206041	3.617366	13.372655	21.960701	6.226934	1.282093	0.497305	0.544487	0.954723	3.397494	1.963874	1.251737	1.822080	2.005539	1.737350	1.665819	1.836932	2.305950	2.616799	1.862589	1.206715	1.517499	1.250918	0.957959	0.964838	1.531618	2.989902	11.269964	18.711271	5.732305	1.855430	0.881193	0.865480	2.499974	2.659899	1.834766	1.567808	1.889928	2.100177	1.413735	1.355412	1.519332	1.759129	1.871682	1.300469	1.291437	1.736174	1.362465	1.312077	1.616137	2.013268	3.555674	17.953964	26.138508	6.095495	1.601341	0.708736	0.485249	0.482436	5.982113	3.875033	2.656477	3.775859	4.926156	4.836329	5.164062	5.025137	6.353260	6.853456	3.555775	2.511031	2.558467	2.510992	2.243123	2.646142	3.724841	6.471524	47.893009	95.020409	28.858139	5.187173	1.659228	1.038577	1.144076	1
33	4.398366	2.433723	2.298009	2.560607	2.051569	1.646811	1.600987	2.660689	3.298986	2.177198	3.010031	2.571338	0.854693	0.899722	1.223100	1.062694	1.731347	4.332378	22.118700	61.207901	24.570139	2.651774	1.255357	0.912837	1.105061	4.910014	2.511932	2.002016	1.968782	1.520011	1.944987	1.441859	1.881458	1.942712	1.380500	1.542284	1.424817	1.271000	1.256461	1.178240	1.200852	1.549232	2.955009	8.641823	19.909277	7.850702	1.385215	1.125310	0.947856	1.291638	3.924685	2.230177	1.733970	1.838024	1.348340	1.472923	1.074613	1.181354	1.666082	1.505705	1.495407	1.532134	1.255239	1.125014	1.104051	1.226197	1.410347	2.904451	8.259642	17.766722	7.825404	1.866951	1.697525	1.818240	3.441523	4.304434	2.167095	1.580785	1.653763	1.024606	1.403814	1.281886	1.552884	1.849052	1.538943	1.359250	1.316907	0.873934	0.945677	1.529008	1.973980	2.438851	3.375411	12.273950	30.518337	9.893764	1.646684	1.179487	0.832089	0.671994	5.835798	3.180518	3.497400	4.064388	3.530075	3.891615	2.834205	4.064425	6.031335	4.305950	5.406380	4.640236	1.778838	1.589263	2.050982	1.959739	2.880089	6.838029	28.777075	84.555206	38.017269	4.652146	2.352100	1.609031	1.702512	1
34	4.100822	4.627549	4.858707	3.148676	1.935762	1.448163	1.934889	2.073253	3.674427	4.570721	2.545599	1.728399	1.649123	1.676324	2.223035	2.587667	2.157445	6.213491	35.123531	95.919731	19.557926	1.454236	0.885944	1.107191	1.017506	4.450980	3.946001	3.296520	2.125837	1.761715	2.057196	1.724886	1.500874	2.286692	2.175632	1.404316	1.012753	1.219121	1.502363	1.498681	1.306626	1.323132	2.601396	11.446574	28.920181	6.730802	1.118776	0.841224	1.154923	1.101428	2.777139	2.716412	2.635645	2.035862	1.337321	1.440176	1.254173	1.493867	1.654615	2.460396	1.909626	1.203333	1.318986	1.124744	1.447254	1.482671	1.479531	2.961791	10.374748	25.499596	6.101970	1.083114	1.010156	2.549159	2.435246	2.213840	2.728067	2.949054	1.824268	1.346942	1.200437	1.135427	1.389759	1.794560	1.968356	1.452845	1.068667	1.134617	1.165900	1.273419	1.925314	2.001703	3.423299	11.425581	28.655539	6.809975	0.906621	0.619350	0.827188	0.782536	4.692598	6.385319	7.229528	5.144094	3.673867	3.032298	4.069848	4.241685	6.806105	8.255481	4.690311	2.750250	2.739663	2.974263	3.345612	3.360287	3.614674	8.136315	45.522644	133.370900	28.885494	2.529813	1.453831	1.843572	1.814158	1
35	4.381784	3.946265	2.315419	1.752654	2.068634	2.106432	1.572575	2.432980	3.496593	3.833798	2.525097	2.045633	1.692067	1.343934	1.198062	1.999590	2.913411	5.061364	31.041910	72.795311	18.264175	2.932272	1.528200	1.303713	1.168944	4.847093	3.415034	2.257385	1.816491	1.667964	1.700389	1.220647	1.383321	1.839387	1.704378	1.091794	1.104719	1.074600	1.204321	1.527440	1.569919	1.805669	3.052813	11.307690	21.991577	6.423347	1.995367	1.195348	1.048054	0.889833	4.251032	2.646353	1.724229	1.939693	1.543446	1.298345	1.404721	1.636470	1.745626	1.750585	1.421058	1.138026	1.176877	0.974576	1.347333	1.711699	1.906247	2.589326	9.853991	19.450428	6.335912	2.531714	1.157830	2.125158	2.346407	2.867039	2.713389	2.207553	1.509195	1.096859	0.968097	0.855610	1.335899	1.709432	1.746063	1.320956	1.329245	1.229265	1.039167	1.199476	1.184179	1.808261	2.719117	11.247439	26.597025	7.715396	1.535575	0.899267	0.764829	0.654148	5.449703	5.811096	3.271418	2.833638	3.320120	3.907292	3.184689	4.981975	6.834134	6.540767	3.649129	2.627350	2.359736	2.153160	2.060115	3.190446	4.250447	6.680965	38.063938	98.049599	28.269171	5.325201	2.500054	1.518277	1.623735	1
36	7.990422	3.468680	2.982875	3.559515	2.706455	1.593579	1.811169	2.381875	3.310432	3.031553	2.304442	1.464111	1.044800	1.004757	1.075761	0.994373	2.192211	5.084818	14.893358	62.300522	33.315910	1.664110	0.861858	1.026826	1.119231	5.772734	2.432442	2.092300	2.282241	2.106630	1.559698	1.257693	1.309632	1.938636	1.891495	1.227019	0.877104	0.706757	0.841826	1.085365	1.582845	1.812236	2.857524	7.159677	19.646030	9.481875	1.045714	0.925469	0.913912	0.721276	3.860362	2.113373	2.162239	2.559172	1.971355	1.352516	1.165436	1.245882	2.061371	2.177178	1.782494	1.126390	0.732652	0.880000	1.023836	1.329207	1.709831	2.928041	7.694571	20.238848	10.168913	1.169178	1.029610	1.229321	1.846650	2.447303	1.318410	1.203965	1.826342	1.512817	1.033465	1.022677	1.050032	1.690422	2.196412	1.410858	1.022398	0.638767	0.554867	0.706230	1.629057	2.347246	3.187888	8.230520	27.383741	12.449926	1.442036	0.815312	0.477792	0.498143	8.054612	3.918212	3.907452	6.008306	4.695370	2.762324	3.371608	4.140625	7.244872	6.372097	4.955056	2.767644	1.788937	1.991026	2.012441	2.257401	4.129168	8.228875	20.409046	89.233833	50.195911	3.155854	1.736144	1.476571	1.473898	1
37	3.255330	3.277806	3.729515	2.515317	1.966389	2.355566	1.792902	2.869001	4.294414	5.055253	5.002210	2.803302	2.049088	1.441133	1.804157	2.530958	3.284789	11.858369	50.543991	56.343441	12.251056	1.461620	1.322920	0.844051	0.560275	3.106053	2.152157	1.871359	1.731235	1.898638	1.868596	1.232943	1.384515	1.859722	2.241432	2.301373	1.555357	1.356247	1.143310	1.251664	1.575745	1.864167	4.759958	15.978674	15.884074	3.533293	0.941850	1.286953	0.925439	0.973351	2.182084	1.778520	2.118216	1.839695	1.807479	1.640454	1.423980	2.062141	2.567325	3.206646	2.897065	1.433852	1.080496	1.036159	1.252379	1.781369	1.878901	4.209477	14.816295	15.917783	4.070677	2.198668	3.708627	2.060778	2.049178	1.444997	1.903218	1.993080	1.449326	1.725804	1.794635	1.614118	1.705142	2.506118	2.713158	2.540270	1.705965	0.977414	0.871181	1.277734	2.241828	2.193423	5.733682	19.715166	23.913744	5.401896	1.477228	0.843096	0.475640	0.607374	2.670224	4.647697	5.787729	4.206016	3.674797	3.896232	3.586829	6.196093	8.732880	10.706122	9.173436	4.452915	3.034433	2.701553	3.257799	3.601937	4.735386	14.355302	62.188751	77.326668	17.689171	3.364010	2.701976	1.476526	1.280231	1
38	5.274297	2.547791	2.059502	2.391544	2.266260	2.611314	2.762384	2.203140	3.074400	2.167772	1.893173	1.646094	1.397755	0.871762	1.443798	1.451143	2.489799	3.781188	21.116741	73.036812	29.590475	3.322324	1.346364	1.431270	1.119484	3.906256	2.436126	1.890531	1.538933	1.774344	1.981188	2.048583	1.931023	1.950842	1.391758	1.022181	1.192481	0.993454	0.819360	1.011596	1.087659	1.513529	2.231266	7.997931	21.605871	8.409323	1.767259	1.036341	1.762105	2.026883	3.497070	2.524581	1.611338	2.027018	2.069454	1.712904	1.688057	1.863168	1.758745	1.259920	1.195891	1.240758	1.172540	1.123358	1.226721	0.920307	1.533872	2.364180	7.109604	19.283503	8.454733	2.432097	1.533289	4.318586	5.444478	3.995587	3.621141	1.747448	2.550060	1.780718	1.493776	1.652989	2.039406	1.933189	1.221942	1.394102	1.381466	1.050153	0.895692	0.982512	1.255486	1.787435	3.391802	9.414495	27.899223	11.463315	1.609619	1.160346	1.269100	0.893422	5.539176	4.180938	3.397896	4.418853	4.572790	4.644881	5.368571	5.301258	6.324912	4.163602	3.864902	2.918015	2.748332	1.762412	2.527132	2.169686	4.436363	6.313912	30.379944	105.202660	45.852108	5.390990	1.836410	2.234541	2.420760	1
39	4.364726	4.185047	3.121884	2.068805	3.164353	2.450759	1.994382	4.065516	2.797085	2.747634	4.743021	2.283190	1.343698	1.538038	1.048570	1.820779	3.051569	2.530930	26.100224	75.314369	14.418118	1.692509	1.301983	1.242115	1.352465	4.676760	3.224278	2.050021	1.632201	1.699821	1.491215	1.483709	1.826155	1.471344	1.624710	2.003114	1.476004	1.308005	1.281003	1.038748	1.284943	1.691931	1.776980	9.215390	24.202417	5.370527	0.984819	1.118860	1.120078	1.005377	3.027266	2.495363	2.541407	2.091409	2.032324	1.588453	1.644480	1.946417	1.766909	2.012094	2.747247	1.366493	1.142020	1.460341	1.665012	1.795645	1.977213	1.967442	9.387252	21.644108	5.081645	1.466601	2.073184	3.335147	4.149787	2.296920	2.303761	2.624722	2.414547	1.955946	1.509261	1.441061	1.835975	1.169635	1.630356	2.270246	1.151643	0.960762	1.222289	1.893970	2.090964	2.883881	2.817065	12.995144	32.592796	6.654177	1.243450	0.849494	0.619990	0.698652	4.984248	4.713858	4.364437	3.599100	5.622557	4.152687	4.057897	7.639430	5.372589	5.821620	8.500659	3.789335	2.378279	2.914625	1.971346	3.563597	5.143764	3.547272	38.777752	109.055470	22.925838	3.266592	2.457271	1.628193	1.699830	1